cross-commodity price transmission and integration … · horisontaalinen hintasiirtymä...

PTT työpapereita 170 PTT Working Papers 170

CROSS-COMMODITY PRICE TRANSMISSION AND INTEGRATION

OF THE EU LIVESTOCK MARKET OF

PORK AND BEEF: PANEL TIME-SERIES APPROACH

Hanna Karikallio

Helsinki 2015

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PTT työpapereita 170 PTT Working Papers 170 ISBN 978-952-224-170-2 (pdf) ISSN 1796-4784 (pdf) Pellervon taloustutkimus PTT Pellervo Economic Research PTT Helsinki 2015

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Karikallio, H. 2015. CROSS-COMMODITY PRICE TRANSMISSION AND INTEGRATION OF THE EU LIVESTOCK MARKET OF PORK AND BEEF: PANEL TIME-SERIES APPROACH. Working Papers 170. 102 p. ISBN 978-952-224-170-2 (pdf), ISSN 1796-4784 (pdf).

Abstract: At this study we analyze horizontal cross-commodity price transmission and

integration of the EU livestock market of pork and beef. The study seeks to investigate

whether or not there are short-term and long-term relationships between pork and beef

prices in the EU. Our focus is on cross-commodity price transmission which provides

valuable insights into market integration and efficiency. We utilize recently developed

panel time-series techniques. Our data consists of monthly data on pork and beef prices

in the EU member states during the period from February 1995 to June 2014. The

estimation results reveal that there exists bi-directional relationship between pork and

beef prices in the EU in long run. Cross-commodity price transmission between pork

and beef has increased remarkably during the past ten years in the EU15 member states.

Also the convergence to the equilibrium has sped up. In short run, we found evidence

only for price transmission from pork prices to beef prices, not vice versa. Overall,

short-run dynamics is significantly different in the EU livestock market compared to

long run dynamics.

Key words: Price transmission, market cointegration, panel time-series, pork and beef

prices

Karikallio, H. 2015. HINTASIIRTYMÄT JA INTEGRAATIO EU:N SIANLIHA-

JA NAUDANLIHAMARKKINOIDEN VÄLILLÄ: PANEELI-AIKASARJOJEN

ANALYYSI. PTT työpapereita 170. 102 s. ISBN 978-952-224-170-2 (pdf), ISSN

1796-4784 (pdf).

Tiivistelmä: Tutkimuksessa tarkastellaan EU:n lihamarkkinoiden integraatiota

analysoimalla tuottajahintamuutosten välittymistä sianliha- ja naudanlihamarkkinoiden

välillä. Tutkimuksessa selvitetään, onko sianlihan ja naudanlihan tuottajahintojen välillä

lyhyen tai pitkän aikavälin yhteyttä ja tasapainoa. Mielenkiinnon kohteena on siis

hintasiirtymät hyödykemarkkinoiden välillä, mikä tarjoaa arvokasta tietoa markkinoiden

integroitumisesta ja tehokkuudesta. Empiirinen analyysi perustuu viime vuosina

kehitettyihin paneeli-aikasarjamenetelmiin. Tutkimuksessa hyödynnettävä aineisto

koostuu EU-maiden sianlihan ja naudanlihan kuukausittaisista tuottajahinnoista 2/1995-

6/2014. Tulokset osoittavat, että sianlihan ja naudanlihan tuottajahinnan välillä on

EU:ssa kaksisuuntainen pitkän aikavälin tasapaino. Hintasiirtymät ovat voimistuneet ja

nopeutuneet huomattavasti viimeisen kymmenen vuoden aikana. Lyhyen aikavälin

tarkastelu osoittaa, että hintasiirtymiä tapahtuu vain sianlihan tuottajahinnoista

naudanlihantuottajahintaan, ei toisinpäin. Kaiken kaikkiaan EU:n lihamarkkinoiden

lyhyen aikavälin hintadynamiikka on hyvin erilainen verrattuna pitkän aikavälin

dynamiikkaan.

Asiasanat: Hintasiirtymä, markkinaintegraatio, paneeli-aikasarja, lihan tuottajahinnat.

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KESKEISET TULOKSET

Tulokset osoittavat, että sianlihan ja naudanlihan tuottajahinnan välillä on EU:ssa

kaksisuuntainen pitkän aikavälin tasapaino. Hintasiirtymät sianlihan ja naudanlihan

välillä ovat merkittävimpiä vanhoissa jäsenmaissa (EU15), mutta hintamuutosten

välittyminen lihamarkkinoilla on tilastollisesti merkitsevää myös uusien jäsenmaiden

sisältyessä analyysiin. Sianlihan tuottajahinnan vaihtelut siirtyvät naudanlihan

tuottajahintaan voimakkaammin kuin naudanlihan tuottajahinnan vaihtelut sianlihan

tuottajahintaan. EU:ssa sianliha hallitseekin lihamarkkinoita. Hintasiirtymät sianlihan ja

naudanlihan markkinoiden välillä ovat voimistuneet ja nopeutuneet huomattavasti

viimeisen kymmenen vuoden aikana. Hintojen konvergoituminen pitkän aikavälin

tasapainoon on kuitenkin edelleen melko hidas prosessi, mikä kertoo EU:n lihamarkki-

noilla vielä olevista markkinoiden integroitumisen vaikeuksista. Lyhyellä aikavälillä

hintasiirtymiä tapahtuu vain sianlihan tuottajahinnoista naudanlihantuottajahintaan, ei

toisinpäin. Kaiken kaikkiaan EU:n lihamarkkinoiden lyhyen aikavälin hintadynamiikka

on hyvin erilainen verrattuna pitkän aikavälin dynamiikkaan. Sianlihamarkkinoita

EU:ssa hallitsevat Tanska, Saksa ja Hollanti. Kyseisten maiden sianlihamarkkinoilla

tapahtuvat hintavaihtelut vaikuttavat voimakkaimmin naudanlihamarkkinoille

välittyvien hintasiirtymien suuruuteen ja nopeuteen. Naudanlihamarkkinoita EU:ssa

hallitsevat vastaavasti Ranska, Irlanti ja Iso-Britannia.

Tulosten perusteella voidaan todeta, että EU:ssa on vuorovaikutteiset ja yhtenäistyvät

lihamarkkinat. Lihamarkkinoiden integraatio on edennyt varsin nopeasti. Maatalous-

tuotteiden kaupan esteiden vähentäminen on keskittänyt toimialaa voimakkaasti.

Unionin sääntelyn on jatkossakin oltava oikeudenmukaista jäsenmaihin nähden:

kaupanesteitä pitää torjua, kilpailuedellytyksiä yhtenäistää sekä huolehtia, että yhteisiä

normeja noudatetaan tasapuolisesti. Kilpailukykyä heikentäviä yksipuolisia velvoitteita

ei tule ottaa käyttöön. Lihamarkkinoiden integroituminen voimistui samaan aikaan, kun

markkinaheilahtelut maataloustuotemarkkinoilla lisääntyivät. Integraatio on näin ollen

edistänyt myös hintaheilahteluiden leviämistä markkinoiden välillä. Markkina- ja

hintariskien kasvaessa niiden hallintaan on panostettava enemmän. Markkinaintegraati-

on näkökulmasta lihamarkkinat EU:ssa ovat toimivat ja tehokkaat. Politiikkatoimenpi-

teiden vaikutukset menevät yhä nopeammin lihamarkkinoiden läpi. Hintaintegraatiosta

johtuen kohdennetuilla politiikkatoimenpiteillä on vaikutusta koko lihamarkkinoiden

toimintaan. EU:n lihamarkkinoita koskevat politiikkapäätökset onkin tehtävä koko

lihamarkkinoiden ja niiden toimivuuden näkökulmasta.

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YHTEENVETO

Markkinoiden integroitumisella on keskeinen merkitys EU:n kasvu- ja kilpailustrategi-

assa. Yhtenäinen sisämarkkina-alue tarjoaa tuotteille laajat markkinat, mutta samalla

lisääntyvä kilpailu pakottaa yhdenmukaiseen hinnoitteluun koko EU:n alueella.

Taustalla on yhden hinnan lain (Law of One Price, LOP) toteutuminen, joka on eräs

kansainvälisen talouden perusoppeja. Sen mukaan samalla tuotteella tulee olla sama

hinta eri maissa, kun kansalliset hinnat ilmaistaan samassa valuutassa.

Tietämys markkinoiden yhdentymisestä ja markkinoiden välisestä vuorovaikutuksesta

on tärkeää maan kilpailukyvyn näkökulmasta. Hintojen välttyminen markkinoilta

toisille heijastaa markkinoiden integraation syvyyttä ja markkinoiden tehokkuutta.

Hintasuhteiden analysointi onkin keskeinen työkalu markkinoiden integraation

analysoinnissa.

Maatalousmarkkinoiden yhdentyminen on ollut aina EU:n keskiössä. Maatalousmarkki-

noiden toimintaedellytyksiin vaikutetaan Euroopan unionin yhteisellä maatalouspolitii-

kalla (Common Agriculture Policy, CAP), jonka tavoitteena on muun muassa

maatalouden tuottavuuden parantaminen, elintarvikemarkkinoiden vakauttaminen ja

kohtuullisten elintarvikehintojen varmistaminen kuluttajille EU:n jäsenmaissa.

Yhteisellä maatalouspolitiikalla integroidaan kansalliset maataloustuotemarkkinat

jäsenvaltioiden sisällä ja välillä, jolloin tuotantokustannuksiin perustuvat kansalliset

hintatiedot välittyvät tehokkaasti muiden EU-maiden maataloustuotemarkkinoille.

Monissa tutkimuksissa on tarkasteltu maataloustuotteiden hintojen välittymistä

kansallisten maatalousmarkkinoiden välillä.1 Useimmissa kyseisistä tutkimuksista myös

päädytään siihen, että maiden väliset hintaerot ovat EU:ssa pienentyneet. Aikasarja-

analyyseihin perustuvien tutkimuksia vaikeuttaa kuitenkin maatalousmarkkinoita

häirinneet toistuvat eksogeeniset shokit, joiden huomioiminen mallinnuksessa on

haastavaa.

1 Esimerkiksi Sanjuan et al.(2001), Serra et al. (2006), Fousekis (2007), Ihle et al. (2012), Liu (2011) ja Meyer (2012).

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Viime vuosina empiirinen tutkimus hintojen välittymisestä elintarvike- ja maatalous-

markkinoilla on saanut paljon huomiota. Hintasiirtymät voidaan karkeasti jakaa kahteen

pääryhmään: vertikaalisiin ja horisontaalisiin hintasiirtymiin. Vertikaalisilla hintasiirty-

millä (vertical price transmission) tarkoitetaan hintojen välittymistä elintarvikeketjun

sisällä. Horisontaalinen hintasiirtymä (horizontal price transmission) taas viittaa hinnan

välittymiseen elintarvikeketjussa samassa asemassa olevien markkinoiden välillä.

Määrittelyyn liittyy markkinoiden rajaaminen jollakin kriteerillä. Yleensä horisontaali-

sella hintasiirtymällä tarkoitetaan hinnan välittymistä tietyin alueen markkinoilta toisen

alueen vastaaville markkinoille (spatiaalinen hintasiirtymä). Se voi kuitenkin viitata

myös hinnan välittymiseen hyödykemarkkinoiden välillä. Kahden tuotteen voidaan

katsoa olevan samoilla markkinoilla ja näin ollen läheisiä substituutteja, jos niiden

hintojen suhde pysyy ajan kuluessa vakaana. Tämä perustuu tuotteiden keskinäiseen

korvattavuuteen, mikä puolestaan riippuu tuotteiden kysyntäfunktioista ja kuluttajien

preferensseistä. Jos tuotteet ovat substituutteja, toisen tuotteen markkinoita häiritsevän

shokin vaikutukset siirtyvät myös toisen tuotteen markkinoille, jos kyseisten tuotteiden

markkinat ovat integroituneet. Hinnan nousu saa tässä tapauksessa kuluttajat

vaihtamaan ostokohteekseen korvaavan tuotteen. Näin ollen läheiset substituutit tulisi

luokitella kuuluviksi samoille markkinoille. Hyödykemarkkinoiden välisten vuorovai-

kutusten ja hintasiirtymien ymmärtäminen muodostuu erityisen tärkeäksi, jos sääntelyllä

halutaan vaikuttaa hyödykemarkkinoiden toimintaan.

Tutkimuksen tavoitteet

Tässä tutkimuksessa tarkastellaan EU:n lihamarkkinoiden integraatiota analysoimalla

tuottajahintamuutosten välittymistä sianliha- ja naudanlihamarkkinoiden välillä.

Mielenkiinnon kohteena on siis hintasiirtymät hyödykemarkkinoiden välillä, mikä

tarjoaa arvokasta tietoa lihamarkkinoiden integroitumisesta ja markkinoiden

tehokkuudesta.

EU:ssa naudan ja sian ruhot luokitellaan standardoidulla laatuluokituksella2. Yhtenäinen

luokittelu on kansallisten lihamarkkinoiden integroitumisen perusta, mutta edelleen

maiden välisiä hintavertailuja vaikeuttaa eri luokkiin kuuluvien eläinten määrien

maakohtaiset vaihtelut. Sen sijaan siipikarjan ruhojen osalta laatuluokittelu ei vielä ole

EU-maissa yhtenäistä, minkä takia tuottajahinnatkaan eivät ole vertailukelpoisia. Tästä

syystä tutkimuksessa onkin rajoituttu tuottajahinnan muutosten välittymiseen

2 Naudan ruhot luokitellaan lihakkuuden mukaan kirjaimin E, U, R, O ja P. Lihasiat luokitetaan Hennessy GP4 -mittarilla.

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sianlihamarkkinoiden ja naudanlihamarkkinoiden välillä ja sipikarjanlihamarkkinat

jätetään analyysin ulkopuolelle3. Tutkimuksessa selvitetään, onko sianlihan ja

naudanlihan tuottajahintojen välillä EU:ssa lyhyen aikavälin tai pitkän aikavälin

yhteyttä ja tasapainoa.

Tutkimuksen merkitys suhteessa olemassa olevaan kirjallisuuteen maataloustuotemark-

kinoiden integroitumisesta ja hintasiirtymistä on kahtalainen. Ensinnäkin tutkimuksen

kohdentuminen hyödykemarkkinoiden välisiin hintasiirtymiin ei ole kirjallisuudessa

yhtä yleistä kuin vertikaalisten ja spatiaalisten hintasiirtymien tarkastelut. Tutkimus

tarjoaakin mielenkiintoista tietoa substituuttihyödykkeiden hintojen välisestä yhteydestä

ja markkinoiden integraatiosta.

Tutkimuksen toinen kontribuutio aihepiirin aikaisempaan tutkimukseen nähden liittyy

tilastolliseen analysointiin ja tutkimuksessa hyödynnettäviin ekonometrisiin menetel-

miin. Tutkimuksen empiirinen analyysi perustuu viime vuosina kehitettyihin paneeli-

aikasarjamenetelmiin. Toisin sanoen hintamuutosten välittymistä lihamarkkinoilla

testataan menetelmillä, jotka hyödyntävät sekä aineiston ajallista vaihtelua että EU-

maiden välistä vaihtelua. Tutkimuksessa hyödynnettävä kuukausitason aineisto koostuu

EU-maiden sianlihan ja naudanlihan tuottajahinnoista ajanjaksolta 2/1995-6/20144.

Tutkimuksen tulokset

Tulokset osoittavat, että sianlihan ja naudanlihan tuottajahinnan välillä on EU:ssa

kaksisuuntainen pitkän aikavälin tasapaino. Tulosten mukaan hintasiirtymät sianlihan ja

naudanlihan välillä ovat merkittävimpiä vanhoissa EU:n jäsenmaissa (EU15), mutta

hintamuutosten välittyminen lihamarkkinoilla on tilastollisesti merkitsevää myös uusien

jäsenmaiden sisältyessä analyysiin. Sianlihan tuottajahinnan vaihtelut siirtyvät

naudanlihan tuottajahintaan voimakkaammin kuin naudanlihan tuottajahinnan vaihtelut

sianlihan tuottajahintaan. EU:ssa sianliha hallitseekin lihamarkkinoita. Tarkastelupe-

riodista riippuen sianlihan tuottajahinnan prosentin nousu nostaa naudanlihan

tuottajahintaa 0.15–0.33 prosentilla. Naudanlihan tuottajahinnan vaihteluiden

siirtyminen sianlihan tuottajahintaan voidaan havaita vain tuloksissa, jotka koskevat

tarkasteluperiodia 1/2007–6/2014. Kyseisen tarkasteluperiodin tulokset osoittavat, että

3 Tutkimuksessa käytetyt lihan laatuluokat: nauta R3-sonni, sika luokka E. 4 Tutkimuksessa hyödynnetään kolmea eri aineistoa EU15 2/1995-6/2014, EU25 1/2005-6/2014 ja EU27 1/2007-6/2014.

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prosentin nousu naudanlihan tuottajahinnassa aiheuttaa 0.1–0.2 prosentin nousun

sianlihan tuottajahinnassa. Tarkastelut pidemmältä periodilta paljastavat, että vain

sianlihan tuottajahinta on välittynyt merkitsevästi naudanlihan tuottajahintaan.

Hintasiirtymät sianlihan ja naudanlihan markkinoiden välillä ovat voimistuneet ja

nopeutuneet huomattavasti viimeisen kymmenen vuoden aikana EU15-maissa. Ennen

vuotta 2005 osaotoksiin perustuvissa estimointituloksissa ei havaittu yhteyttä sianlihan

ja naudanlihan tuottajahintojen välillä. Kyseisen vuoden jälkeen EU:n lihamarkkinoiden

integroituminen on ollut nopeaa ja hintasiirtymät tuottajahintojen välillä ovat

tilastollisesti merkitseviä. Lihamarkkinoiden integroitumisen voimistuminen on

nähtävissä myös EU25- ja EU27-maita tarkasteltaessa.

Sianlihan ja naudanlihan tuottajahintojen ajautuessa pois pitkän aikavälin tasapainosta,

kestää tasapainon uudelleen saavuttaminen EU15-maissa noin vuoden. Ennen vuotta

2005 tasapaino saavutettiin uudestaan noin 1,5 vuoden hintojen sopeutumisella. Pitkän

aikavälin tasapainon savuttaminen onkin nopeutunut selvästi viime vuosina. Hintojen

konvergoitumisen voidaan kuitenkin katsoa olevan edelleen melko hidas prosessi, mikä

kertoo EU:n lihamarkkinoilla vielä olevista markkinoiden integroitumiseen vaikuttavis-

ta hidasteista ja jäykkyyksistä.

Lyhyellä aikavälillä tulokset osoittavat, että hintasiirtymiä tapahtuu vain sianlihan

tuottajahinnoista naudanlihantuottajahintaan, ei toisinpäin. Lisäksi lyhyen aikavälin

hintasiirtymiä ei voitu havaita EU:n lihamarkkinoilla ennen vuotta 2007. Tulokset myös

osoittavat, että havaittu lyhyen aikavälin hintasiirtymä sianlihan tuottajahinnasta

naudanlihan tuottajahintaan on negatiivinen. Tämä kertoo ennen muuta markkinoiden

reagointikyvyssä olevista viiveistä, jota ovat naudanlihamarkkinoilla suuremmat kuin

sianlihamarkkinoilla. Kaiken kaikkiaan EU:n lihamarkkinoiden lyhyen aikavälin

hintadynamiikka on hyvin erilainen verrattuna pitkän aikavälin dynamiikkaan.

Tulosten mukaan sianlihamarkkinoita EU:ssa hallitsevat Tanska, Saksa ja Hollanti.

Kyseisten maiden sianlihamarkkinoilla tapahtuvat hintavaihtelut vaikuttavat

voimakkaimmin EU:n naudanlihamarkkinoille välittyvien hintasiirtymien suuruuteen ja

nopeuteen. Tanska, Saksa ja Hollanti ovatkin selvästi lisänneet EU:n lihamarkkinoiden

integraatiota; EU-maiden naudanlihamarkkinat ovat selvimmin integroituneet kyseisten

maiden sianlihamarkkinoihin. Naudanlihamarkkinoita EU:ssa hallitsevat tulosten

mukaan vastaavasti Ranska, Irlanti ja Iso-Britannia. Sen sijaan ainakin Kreikan,

Kyproksen, Slovenian ja Slovakian sianlihan ja naudanlihan tuottajahintojen vaihtelut

vaikuttavat negatiivisesti tuloksiin EU:n lihamarkkinoiden integraatiosta. Kyseisten

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maiden lihamarkkinoilta lähtevät hintavaihtelut välittyvätkin vain heikosti EU:n

lihamarkkinoille.

Johtopäätökset

Johtopäätöksinä tutkimuksen empiirisistä tuloksista voidaan ensinnäkin todeta, että

EU:ssa on vuorovaikutteiset ja yhtenäistyvät lihamarkkinat. Lihamarkkinoiden

integraatio on edennyt varsin nopeasti. Maataloustuotteiden kaupan esteiden

vähentäminen on keskittänyt ja keskittää edelleen toimialaa voimakkaasti. Unionin

sääntelyn on jatkossakin oltava oikeudenmukaista jäsenmaihin nähden: jäsenmaiden

välisiä kaupanesteitä pitää torjua, kilpailuedellytyksiä yhtenäistää sekä huolehtia, että

yhteisiä normeja noudatetaan tasapuolisesti. Kilpailukykyä heikentäviä yksipuolisia

velvoitteita ei tule ottaa käyttöön.

Maataloustuotemarkkinoiden toimintaympäristöä kuvaa nykyään nopeat ja merkittävät

heilahtelut. Suuret hintavaihtelut ovat tulleet osaksi markkinoiden toimintaa.

Lihamarkkinoiden integroituminen voimistui samaan aikaan, kun markkinaheilahtelut

lisääntyivät ja niiden merkitys toimialalla kasvoi. Integraatio on näin ollen edistänyt

myös hintaheilahteluiden leviämistä markkinoiden välillä. Markkina- ja hintariskien

kasvaessa niiden hallintaan on panostettava integroituvassa maataloudessa enemmän.

Markkinaintegraation näkökulmasta lihamarkkinat EU:ssa ovat toimivat ja tehokkaat.

Politiikkatoimenpiteiden vaikutukset menevät yhä nopeammin läpi koko lihamarkkinoi-

den. Hintaintegraatiosta johtuen kohdennetuilla politiikkatoimenpiteillä on vaikutusta

koko lihamarkkinoiden toimintaan. EU:n lihamarkkinoita koskevat politiikkapäätökset

onkin tehtävä koko lihamarkkinoiden ja niiden toimivuuden näkökulmasta.

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TABLE OF CONTENTS

Yhteenveto ......................................................................................................................... 5

1 Introduction .............................................................................................................. 15

2 Background............................................................................................................... 18

2.1 Market integration and horizontal price transmission ....................................... 18

2.2 Meat market in the EU ...................................................................................... 20

2.3 Modelling price transmission in the panel framework ...................................... 22

3 Econometric methodology........................................................................................ 25

3.1 Panel unit root tests ........................................................................................... 25

3.1.1 First generation panel unit root tests (cross-country independence) ... 26

3.1.2 Second generation panel unit root tests (cross-country dependence) ... 30

3.1.3 Panel unit root tests allowing structural breaks .................................... 32

3.2 Cointegration tests ............................................................................................. 35

3.3 Estimation methods ........................................................................................... 43

4 Data description ........................................................................................................ 51

5 Empirical results ....................................................................................................... 57

5.1 Panel unit root results ........................................................................................ 57

5.2 Cointegration test results ................................................................................... 65

5.3 Estimation results .............................................................................................. 73

5.4 Robustness check against estimation method ................................................... 81

5.5 Robustness check against definitions of the data sample .................................. 83

6 Concluding remarks.................................................................................................. 86

References ....................................................................................................................... 89

Appendix 1. LM unit root test results for individual EU member states assuming

one structural breakpoint. .................................................................................. 96

Appendix 2. Pooled Mean Group ECM estimates, EU15 over different sub-periods97

Appendix 3. Cross-sectional stabilities of the long-run elasticity and the speed of

adjustment ......................................................................................................... 98

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1 INTRODUCTION

In recent years, the empirical research on agricultural price transmission has gathered

considerable attention. It is frequently utilized concept in market integration analysis.

Price transmission can be separated into two types: horizontal and vertical. Vertical

price transmission refers to price linkages along a supply chain, whereas horizontal

price transmission means the price linkage occurring among different markets at the

same position in the supply chain.

The literature analyzing vertical price linkages has concentrated on evaluations of the

links between farm, wholesale and retail prices. The asymmetric vertical price

transmission, i.e., increasing and decreasing prices at one level of supply chain transmit

at different rates to another level, has aroused considerable discussion in agricultural

economics (Vavra and Goodwin, 2005). The price relationships along the supply chain

provide insights into marketing efficiency and consumers’ and farmers’ welfare (Aguiar

and Santana, 2002). Meyer and von Cramon-Taubadel (2004) and Frey and Manera

(2005) provide reviews of the literature on asymmetry price transmission.

The notion of horizontal price transmission usually refers to price linkages across

market places (spatial price transmission). However, it can also concern the transmis-

sion across different agricultural commodities (cross-commodity price transmission)5,

from non-agricultural to agricultural commodities (for example from energy prices to

agricultural prices; Serra et al., 2010 and Hassouneh et al., 2012), and across different

purchase contracts for the same commodity (for example from futures to spot markets

and vice versa; Baldi et al., 2013).

The key underlying theoretical explanation of spatial price transmission is the spatial

arbitrage and the consequent Law of One Price (LOP)6. On the contrary, for cross-

commodity price transmission, the co-movement of prices is mostly driven by the

substitutability and complementarity relations among the products (Saadi, 2011), which,

in turn, depends on the respective demand functions and on the underlying preferences.

Substitutability and complementarity essentially implies that the prices of two

commodities have a long-run relationship and shocks to one of the markets will get

transmitted to another if markets are integrated. Understanding of inter-commodity

price relationships and shock transmissions becomes important when price volatility and

5 Detailed discussion: Esposti and Listorti (2012, 2013). 6 The Law of One Price (LOP) is an economic concept which posits that "a good must sell for the same price in all locations". The intuition behind LOP is based on the assumption that differences between prices are eliminated by market participants taking advantage of arbitrage opportunities.

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market failures formed the basis for public interventions. Even though, the background

theory differs between spatial and cross-commodity price transmission, the empirical

framework and the econometric implications of these different cases of horizontal price

transmission are the same.

At this study we analyze horizontal cross-commodity price transmission and integration

of the EU livestock market of pork and beef. The study seeks to investigate whether or

not there are short-term and long-term relationships between pork and beef prices in

EU. The contribution of this study to the existing literature on the price transmission in

agricultural market is twofold. The first contribution is the focus on cross-commodity

price transmission. Studies on this field are not as frequent as studies on vertical or

spatial price transmission although they provide valuable insights into market

integration.

The second contribution of our study refers to econometric methods: we utilize recently

developed panel time-series techniques. In other words, to study the causality

relationship between pork and beef prices, we catch both time-series and cross-country

variation in our data set. Our data consists of monthly data on pork and beef prices in

the EU member countries during the period from February 1995 to June 2014.

Panel models have many advantages compare to time series or cross-section models:

panel models make more information available, hence more degrees of freedom and

more efficiency. They allow controlling for individual heterogeneity. They also allow

identifying effects that cannot be detected in simple time series or cross-section data.

One benefit is better properties of the testing procedures when compared to more

standard time series methods. In the panel framework, we can analyze long-run

relationship across the panel while allowing the associated short-run dynamics and fixed

effects to be heterogeneous across different members of the panel (Banerjee, 1999;

Maddala and Wu, 1999).

To examine the linkages between pork and beef prices, we follow the standard three-

step approach consisting of (i) assessing the stationarity of the time series, (ii) in case

the variables are not stationary, checking whether they are characterized by a

cointegration relationship, (iii) in case cointegration holds, testing for long-run and

short-run relationships between pork and beef prices by estimating error correction

mechanism (ECM), which permits to analyse the long-run relationship between the

variables jointly with the short-term adjustment towards the long-run equilibrium.

In analyses of the price linkages in the meat sector, panel causality approach has been

utilized mainly in vertical price transmission studies, for example, Boetel and Liu

17

(2008), Goodwin and Holt (1999), Goodwin and Harper (2000) and Carraro and

Stefani (2010).

In our study, testing for unit root is performed using the panel unit root test of Levin,

Lin and Chu (2002), Im, Pesaran and Shin (2003), Breitung (2000), Maddala and Wu

(1999), Choi (2001), Pesaran (2003) and Im, Lee and Tislau (2005, 2010). The panel

cointegration test based on Pedroni (1999, 2004), Kao (1999), Westerlund (2005, 2007),

Westerlund and Edgerton (2008) are considered. In addition, we carry out Johansen’s

Fisher panel cointegration test suggested by Maddala and Wu (1999). Moreover, Mean-

Group estimator (MG) by Pesaran and Smith (1995), Pooled Mean Group estimator

(PMG) by Pesaran, Shin and Smith (1999), group-mean Dynamic OLS (DOLS) and

group-mean Fully Modified OLS (FMOLS) by Pedroni (2000) are implemented to test

for Granger-causality in panel models. We use the software Stata for empirical analyses.

The main objective of the study is to analyze the dynamics of meat price transmission

and market integration between pork and beef in the EU. The specific objectives are:

- To examine the trend in prices of pork and beef in the EU,

- To analyze the price integration between pork and beef markets in the EU,

- To identify the long and short run price transmission of pork and beef in the EU.

The rest of the paper is organized as follows: In the next section we briefly discuss on

the concept of market integration and price transmission and how it can be modelled in

the panel framework. We also take a quick snapshot on the EU meat market. In section

3 we describe the theory of the econometric methods we have utilized. Section 4 defines

the data and its limitations. Section 5 gives the results and discussion and the last

section concludes.

18

2 BACKGROUND

2.1 Market integration and horizontal price transmission

Market integration is an indicator that explains how much different markets are related

to each other. Price transmission reflects the extent of market integration and the extent

of market efficiency. Consequently, analysis of relationships between prices is a

common tool in market integration analysis. Even though theory in general argues that

other variables (product attributes) are equally important in describing and explaining

market integration, this interest in prices can be justified because data on prices are

easier to obtain, and often the only data available.

Price transmission can be understood from two aspects: vertical and horizontal. The

literature analyzing vertical price linkages has concentrated on evaluations of the links

between farm, wholesale and retail prices. In general, the primary attention in studies

that analyze vertical price transmission is the magnitude, speed and nature of the price

adjustments through the supply chain and especially the extent to which adjustments are

asymmetric.7

Although studies into vertical price transmission are the most frequent, horizontal price

transmission is the focus of this study. The literature analyzing horizontal price linkages

dates back more than one-hundred years and is typically concerned with links between

prices at different locations (spatial price arbitrage). Spatial price arbitrage is an

equilibrium concept, where in a well-functioning market, transactions between spatially

dispersed agents will ensure that price shocks occurring in one market evoke responses

in other market places (Serra et al., 2006). In theory, prices of homogeneous goods in

two separate locations will differ by, at most, the cost of moving the commodity form

the cheapest market place to the most expensive one. This arbitrage condition is

equivalent to the weak version of the Law of One Price (LOP) (Fackler and Goodwin,

2001). Numerous concepts such as market integration and market efficiency have been

used to describe spatial price arbitrage (Serra et al., 2006 and Fousekis, 2007). The

majority of the studies on spatial price relationships have been focused on food and raw

material markets.

7 Studies on vertical price transmission: Peltzman (2000), Meyer and von Cramon-Taubadel (2004), McCorriston and Sheldon (1996), Vavra and Goodwin (2005).

19

Markets are not related only between regions but also between commodities. Horizontal

price transmission also concerns the transmission across different agricultural

commodities (cross-commodity price transmission) (Esposti and Listorti, 2013). In this

case of horizontal price transmission the co-movement of prices is mostly driven by the

substitutability and complementarity relations among the commodities (Saadi 2011),

which, in turn, depends on the respective demand functions and on the underlying

preferences. However, the question is not whether the two commodities are comple-

ments or substitutes, but what degree they are first and what degree they are second.

When goods and information flow freely, shocks occurring in one market will evoke

responses in other markets. In cross-commodity price transmission studies are often

examined the question of how shocks in one of the commodity markets will get

transmitted to the rest if markets are integrated. As the price of one commodity

increases following an international price shock, demand may shift towards another

commodity resulting in higher prices on these as well. Thus, stabilization of prices of

essential agricultural commodities continues to remain an area of major concern for

policy makers. An understanding of inter-commodity price relationships and shock

transmissions forms the basis for public interventions (Alderman, 1993; Rashid, 2011;

Sharma and Kumur, 2001). Price instability affects both producers and consumers and

has macroeconomic implications as well. World widely, this was an important aspect

during the global food crisis from the early 2007 to the middle 2008, during which the

prices of a number of food commodities increased sharply. It was followed by a period

of collapsing prices in the second half of 2008.

There are only few studies on cross-commodity prices transmission. For example,

Asche et al. (2005) examined cross-commodity market integration between wild and

farmed salmon on the Japanese market and found that the species were close substitutes

on the market, and that the expansion of farmed salmon had resulted in price decreases

for all salmon species. Nielsen et al. (2007) found that markets for farmed trout are

related toothed fish markets in Germany, and that markets for these trout are more

closely linked to markets for captured fish than to farmed salmon. Timmer (2009)

addresses the long-run relationships among the prices of the three basic cereal staples,

rice, wheat and corn (maize) in Asian market. He found that wheat and corn are more

closely connected with each other than with rice markets. Rashid (2011) examined inter-

commodity price relationships to assess the relative importance of each of the three

major cereals (maize, wheat, teff) in Ethiopian cereal markets. He concluded that maize

is the most significant in exacerbating price variability with respect to the persistence of

shocks to itself and the two other cereals. He noticed that focusing on maize helps to

stabilize prices and also to reduce costs of stabilization. Sharma and Kumur (2001)

found complex long run relationships among rice, wheat, groundnut seed, mustard seed,

cottonseed, groundnut oil, vanaspati oil, mustard oil and other edible oils in Indian

20

market. They concluded that simply analyzing the price behavior of one commodity

while ignoring the behavior of the prices of substitutes will not be meaningful for price

stabilization purposes. The earlier researches includes for example Alderman (1993)

who investigated how information is transmitted across commodities (maize, millet,

sorghum) in Ghana. He noted imperfections in the way markets process information: the

lagged price of maize conveys information that is not contained in the past price of

sorghum or millet.

Although the studies specifically related to cross-commodity price movements are only

a few, after the biofuels issue has being raised by the recent food crisis, a considerable

number of papers have been dealing with the impact of crude oil on major agricultural

commodities.8

2.2 Meat market in the EU

Europe constitutes the largest single market in the world and at the same time qualifies

as one of the largest agricultural exporters in the world. Due to the growing liberaliza-

tion of world markets and the continuing European integration, agriculture in the EU is

undergone and is still undergoing constant restructuring in order to meet the demands of

global competition. The meat industry is very competed branch of industry and is

currently in its mature stage of development. Large multinational firms are prominent in

the meat market. The success of the largest firms is linked to their ability to achieve

economies of size and scope.

For decades, the meat sector has been one of the most important of the European

Union’s (EU) agriculture. Half of all the EU farms have livestock. The EU also provides

large support to livestock sector.

With approximately 150 million pigs and a yearly production of about 20 million

carcass weight the EU is the world’s second biggest producer of pig meat after China

and also the biggest exporter.9 The EU's main producer countries are Germany, Spain

and France. They represent together already half of the EU's total slaughter. Although

the pig herd in EU has been decreasing since 2006, the EU has a self-sufficiency of

about 110% and exports about 12% of its total production. Especially the Danish and

8 For example Arshad and Hameed (2009), Saghaian (2010), Baffes (2007), Muhammad and Kebede (2009), Bakhat and Wurzburg (2013), Ciaian and Kancs (2011) and Kristoufek, Janda and Zilberman (2012). 9 These rounded numbers has been valid during the last 5 years (2008-2013).

21

Dutch pork industries are export driven. Denmark, for example, is one of the world’s

largest pork exporters with over 75% of its output going to some 100 countries. Main

export destinations are Russia and East Asia, in particular China.

The EU is the world’s third largest producer of beef after the USA and Brazil. Also the

beef sector is important to the EU. With a herd of around 85 million heads and a total

yearly production of about 8 million tons of beef, the EU had a self-sufficiency of

approximately 100%. France, Germany, Italy and UK are the main producing member

states. These four member states represent together slightly more than 56% of the EU

total production. However, similarly to pork production, beef production in Europe has

declined, mainly due to disease crises and the consequent reduction in demand. In

Europe beef production is to a large extent a by-product of milk production with around

two-thirds of beef production coming from the dairy herd. Thus, the EU’s milk

production quota system affects also directly to beef production.

The EU limits meat imports with high tariffs and a complex set of quotas. In addition,

the EU has introduced sanitary barriers unrelated to the spread of disease among meat

animals. Strict regulations on slaughter and processing plants and a decision to ban

imports of meat from animals that received hormones in their feed have placed strong

restrictions on trade. The net effect of the meat barriers is to limit imports to special,

country-specific quotas, with small imports outside the quotas (Dyck and Nelson,

2003).

The integration of agricultural markets has always been very important for the member

states of the EU. Therefore, they merged the organization of the sector in the (Common

Agricultural Policy, CAP). Under the EU’s Common Agricultural Policy, the

agricultural markets are required to become spatially integrated within and between all

member states. In an integrated market, price information related to the production costs

should be efficiently transmitted between the member states. Specially, the pork market

is said to be homogenous because of its concentration on some dominating countries.

Several authors found a stable long-run equilibrium using cointegration tests on small

samples of countries, for instance Sanjuan and Gil (2001) and Serra et al. (2006).

Nevertheless, investigating a larger sample of 14 EU countries, Fousekis (2007) found

that the prices are not homogenous. The bovine market of the EU is more heterogeneous

due to the disturbances caused by policy measures (CAP) and a large variety of

production strategies (e.g. suckler cow husbandry, bull-mast or as a side product of milk

production). Because of a large number of exogenous shocks, such as policy changes,

the standard cointegration measures are problematic. For that reason Ihle et al. (2012)

used a methodology that is robust for break points and found cointegration between the

calf prices of four EU countries.

22

The developmental steps towards market efficiency in the EU pork and beef markets are

analyzed by Liu (2011). She evaluated the extent to which the Finnish domestic meat

market responds to changes in the European price (German and Danish prices).

According to Liu, both pork and beef prices in Finland are found to have slowly

cointegrated with German prices, but the cointegration relationship of the two counties

is only found to be symmetric for pork prices, while it is asymmetric for beef prices.

Producer price for pork in Finland is symmetrically cointegrated with the Danish price,

but the Finnish and Danish beef prices show a random walk. This implies that the price

transmission to the Finnish pork producer market from the EU market is smoother and

more efficient than for the beef market. However, the speed of transmission is still slow

compared to that between the Danish and German markets.

Meyer (2012) applied panel convergence tests with individual adjustment processes in

order to analyze the convergence of the EU livestock markets of pork and beef. The

estimation results reveal that the overall price heterogeneity is reducing within the EU

for both beef and pork. Meyer confirms that exogenous changes, such as the

enlargement and policy measures (European Food Monitoring Tool and Mid-Term

Review) improved the functioning of the internal markets. According to the study, larger

heterogeneity of the beef prices is the result of the still remaining differences of policy

measures within the member states of the EU. Focusing on the analysis of the EU

enlargement in 2004, the study found a stronger convergence of the prices of the new

member states compared to the old member states, which confirms that the accession

countries are catching-up. Nevertheless, the prices in the more segmented beef markets

of the accession countries were less strongly progressing towards homogeneity. A

detailed study of the EMU and non-EMU countries indicated that the dropping of the

currency risk has indeed had an influence on agricultural markets. The countries within

the Eurozone converge faster than the other countries, which significantly reduced the

welfare losses of consumers and producers. The study also reveals that at the beginning

of the ongoing euro crisis the prices within the EU only temporarily dispersed, but then

converged again.

2.3 Modelling price transmission in the panel framework

Let us consider agricultural prices observed over three different dimensions: space,

commodity and time. The generic price is pi,k,t where: i=1,…,j,…,N is the market place

(spatial dimension); k=1,…,h,…,K is the commodity; t=1,…,s,…,T is the period of

observation (time dimension). By more conventionally distinguishing between a cross-

sectional dimension, given by the combination of the dimensions ik, and a time

23

dimension t, we can identify any generic price observation as pik,t (scalar) and any

generic price series (vector) as pik . Notice that here we consider the logarithms of

prices. This monotonic transformation facilitates the economic interpretation of results,

in particular considering that regression coefficients may be interpreted as elasticities.

Consequently, henceforth pik identifies the time series of the price logarithm of the kth

commodity in the ith market place.

The behavior of pi,k,t over its three dimensions can be represented within suitable

structural models as the combination of market fundamentals such as supply, demand

and stock formation. However, such models are complex and hardly tractable in the

empirical analysis, whereas the investigation of price evolution and linkage is more

frequently afforded within reduced-form models. When all the three dimensions are

explicitly considered, reduced-form models are actually and by far more feasible and of

immediate use to generate price predictions, i.e. the estimation of E(pi,k,t pj,k,t-s, pi,h,t-s,

pi,k,t-s), given the available observations.

By distinguishing a cross-sectional dimension, ik, and a time dimension, t, a generic

reduced-form model of price formation and transmission over these two dimensions is

the following:

��,� = �� + ∑ ��,�� + ∑ ∑ � ��,��

��

�� ,�� + ��,� (1.1)

where S is the maximum time lag and ��,� ~ �(0, ��,�� ). In a more compact matrix

form equation can be written as:

� = � + ∑ �� + �� (1.2)

where P, Ps and t are (T×(N×K)) matrices, s expresses the time lag, α is a (T×(N×K))

matrix of time invariant parameters, that is ��,� = ��,�� = ��, ∀�, �, � (any column

of α contains T elements with constant value αik) and ��~ �(�, ��). Ws is a ((N×K) ×

(N×K)) matrix of unknown parameters incorporating the correlation across prices

within both the time and cross-sectional (space-commodity) dimensions. The diagonal

elements, � ��,�� , indicate the auto-correlation over time, with the exclusion of the matrix

W0, where diagonal elements are evidently � ��,�� = 0 ∀ ik. The off-diagonal elements,

� ��,�� , represent the cross-sectional dependence of prices; in other words, they express

the interdependence among the different prices and, therefore, the degree and the

direction of transmission of the price shocks.

In particular:

24

- if h=k but i≠j, we are considering the price transmission for the same commodity

across space, that is, different market places. In this case, under perfect spatial

arbitrage, the validity of the Law of One Price (LOP) implies that � ��,�� = 1;

- if i=j but h≠k, we are considering the price transmission between two different

commodities in the same market. In this case, elements � ��,��indicate the degree

of substitutability between the different goods. � ��,�� will be close to 1 (-1) un-

der perfect substitutability (complementarity) between h and k, while it will be

close to 0 under low substitutability (complementarity).

As pik indicates the logarithms of prices, the elements of Ws express the price

transmission elasticities. Within the logarithmic form, the implicit assumption is that all

factors possibly contributing to price differentials but not explicitly taken into account

in the model (for example, transportation and transaction costs) are a constant

proportion of prices.

These constant multiplicative terms (that can be naturally intended as percentages)

apply to price pik,t-s, to obtain pjh,t and are captured by the elements of α.

If the matrix of unknown parameters, Ws, contains all the information about price

linkages over the three dimensions, we can expect that the transmission equations (1.1-

1.2) get rid of possible autocorrelation and heteroskedasticity across both the time and

cross-sectional dimensions; we can assume that spherical error terms are restored:

�� ~ �(�, ��) and E(t, t-s) = 0. The proper specification of (1.1-1.2) aims indeed at

restoring such conditions.10

10 More detailed discussion: Esposti and Listorti (2013)

25

3 ECONOMETRIC METHODOLOGY

In this section we represent econometric methods we have utilized: panel unit root tests,

panel co-integration tests and the panel data techniques to estimate the long-run and

short-run relationships between price series.

3.1 Panel unit root tests

The unit root test is an important tool in econometric analysis to test whether a time

series is stationary or not. It is known that univariate unit root tests are of low power

when the sample size is medium or small. To overcome this problem, testing the unit

root in the panel setting was developed and has been intensively studied in the last two

decades.

A variety of procedures for the analysis of unit roots in a panel context have been

developed. The logic behind the use of a panel unit root test is to combine the

information from time series with the information from cross-sectional units. Since the

power of unit root tests depend on the total variation in the data used (both in the

number of observations and their variation), panel unit root tests are more powerful than

standard time-series unit root tests. The variation across cross-section units improves

estimation efficiency, leading to smaller standard errors and, consequently, to higher t-

ratios and potentially more precise parameter estimates. With the increasing availability

of quite rich panel data sets in a number of contexts, these kinds of tests would seem

very attractive. One of the advantages of panel unit root tests is that their asymptotic

distribution is standard normal. This is in contrast to individual time series unit roots

which have non-standard asymptotic distribution.

However, a variety of issues arise when panel data are employed in testing for unit

roots. Early panel unit root tests generally ignore cross-sectional dependence that is

common for most of the macroeconomic series. Despite the shortcoming of early unit

root tests, they have helped researchers to develop new tests to deal with dependence

across cross-section units. These recent tests try to find out the same question of non-

stationarity with early ones but have different approaches to resolution and different

implications for empirical research. Therefore, in order to compare results predicated by

26

these tests and to discover what the impact of cross-sectional dependence assumption is

on unit root testing, we use more than one unit root tests. The first-generation tests we

conduct are Levin, Lin and Chu (2002), Im, Pesaran and Shin (2003), Breitung (2000),

Maddala and Wu (1999) and Choi (2001). After the first-generation unit root tests, we

conduct Pesaran’s (2003) second-generation unit root test.

We also employ unit root test based on the Lagrangian multiplier (LM) principle

developed by Im, Lee and Tislau (2005, 2010) which applies when a structural break

occurs at different time period in each time series as well as when the structural break

occurs in only some of the time series. The proposed test is not only robust to the

presence of structural breaks, but is also powerful in the basic case where no structural

breaks are involved.

3.1.1 First generation panel unit root tests (cross-country independence)

Levin, Lin and Chu (2002)

Levin, Lin and Chu (2002) (LLC ) suggest a more powerful panel unit root test than

performing individual unit root tests for each cross section. The null hypothesis of LLC

test is that each individual time series contains a unit root against the alternative that

each time series is stationary. The structure to be tested has the following form, similar

to an Augmented Dickey-Fuller (ADF) test but into a panel framework:

Δyit = ρiyi,t-1 + ∑ �� iLΔyi,t-L + αmidmt + εit, m = 1,2,3 (3.1)

where y is the variable to be tested for unit root, Δ is the first difference operator and pi

is the lag order, which is allowed to vary across cross sections and is determined into the

test procedure. These terms are included to take into account heterogeneous serial

correlation across cross-sectional units; dmt can take three values depending on the

model specification: d1t ={empty set}, d2t ={1} including an individual constant and d3t

={1, t} including an individual constant and an individual linear trend. ρi, �iL and αmi are

parameters to be estimated. it is an error term that is assumed to be independently

distributed across i and t, i = 1,..., N, t = 1,..., T. Levin Lin and Chu tested the null

hypothesis: ρi = ρ = 0 for all i against the alternative hypothesis: ρi = ρ < 0 for all i. The

test based on the test statistic )ρ̂/se(ρ̂t*ρ (where ρ̂ is the OLS estimate of ρ in

equation (3.1), and )ρ̂se( is its standard error). Levin, Lin and Chu (2002) showed that

the estimator ��∗

is asymptotically normal distributed as N(0,1) .

27

Im, Pesaran and Shin (2003)

As stated before, LLC test is restrictive in the sense that it requires ρ to be homogeneous

across individuals. Im, Pesaran and Shin (2003) (IPS) permit a heterogeneous

coefficient on yi,t-1, proposing an alternative testing procedure that averages the

individual unit root test statistics. Im, Pesaran and Shin begin by specifying a separate

ADF regression for each cross section with individual effect and no time trend. The

estimated model is also the one given in equation (3.1). However, the null hypothesis is

that each series in the panel has unit root ρi = ρ = 0, and the alternative hypothesis states

that some individual series have unit roots while some are stationary. It can be

expressed as ρi < 0 for i = 1, 2,…, N and for i = N + 1,…,N. Thus, the null hypothesis of

this test is that all series are non-stationary process under the alternative that fraction of

the series in the panel are assumed to be stationary. Therefore, the null is rejected if

there is a subset (N1) of stationary individuals.

The first test (IPSLM) that they propose is the standardised group-mean Lagrange

Multiplier (LM) bar test statistic which allows different serial correlation patterns across

cross-section units:

,

11

11

iNi

iNi

LMLMVarN

LMENLMN

(3.2)

with iNi LMNLM

1

1 , where LMi denotes the individual LM test for testing i = 0

in the equation (3.1). E(LMi) and Var(LMi) are obtained by Monte Carlo simulation. The

second test (IPSt) is a standardised group-mean t bar test statistic, with an expression

similar to the equation (3.2) but replacing LM and LMi by t and it , respectively. The

IPS statistic is defined as the average of all the N individual ADF statistics:

�̅ =�

��

��

�� (3.3)

where �� denotes the individual pseudo t-ratio for testing i = 0 in (3.1), and E(ti) and

Var(ti) are obtained using Monte Carlo simulation. Im et al. show that when the lag

order is non zero for some cross-sections, and as N ∞, T ∞ and kTN / , the

limiting distribution of both test statistics is normal N(0,1).

Im et al. show that if we select a large enough lag order for the ADF regressions, the

small sample properties of IPS test outperform those from LLC test. However, they

found that both LLC and IPS tests present important size distortions when either N is

small or N is relatively large with respect to T.

28

Besides the popular LLC and IPS tests, we performed three more sophisticated first

generation panel unit root tests which try to correct some flaws that the former tests may

present. They are the Breitung (2000) test which shows a higher power than LLC or

IPS tests when they are compared in Monte Carlo experiments and the Maddala and

Wu (1999) and Choi (2001) Fisher type tests, which can be applied using ADF or

Phillips-Perron (PP) versions of the unit root tests for each cross section, and is also

found to be superior to the IPS test.

Breitung (2000)

Breitung (2000) consider the same basic ADF specification as Levin at al. (2002).

However, Breitung finds that both the LLC and IPS tests suffer from a substantial loss

of power if individual-specific trends are included. This is due to the bias correction that

also removes the mean under the sequence of local alternatives. He proposes a test

statistic whose power is substantially higher than that of LLC or IPS. Breitung considers

a panel unit root test which employs unbiased t-statistics. Breitung’s test assumes the

unit root process is common to all cross sections. By allowing for heterogeneous

deterministic trends and short-run dynamics across countries without the need of bias

adjustment, the Breitung test has more power to reject a false null and is not sensitive to

the degree of augmentation of the ADF specifications

The panel data yit is generated by the simple components model:

yit = µi + βit xit, (3.4)

where the unobserved error term xit follows

xit = i xit-1 + it. (3.5)

The null hypothesis is to test the presence of a unit root in all cross-sectional units, i =

1 for all i. For this, it is assumed that it ~iid(0,σ2 ) with E(εit4), the initial observations xi0

are independent and identically distributed (i.i.d.) across i with E(ε0t4 ) < ∞ and

independent of it for all t ≥ 1 and i.

This testing problem is invariant to the following linear transformation:

yit*=y

it+ μ

it * + β

i*t. To construct a test that is invariant to the transformation, Breitung

suggested the use of the transformed data:

(Δyit)* = st[Δyit – (1/T-t)(Δyi,t+1 + … + ΔyiT)], (3.6)

29

for t = 1,..,T – 1, where �� = (T-t)/(T-t+1) and ��

∗ = yi,t-1 – yi0 – ((t-1)/T(yiT – yi0) for t =

2,…,T. The panel unit root test for the null hypothesis proposed by Breitung follows

statistic:

BnT = (

�

√��) ∑ ∑ �∆��,��

∗��

�� ∗

�,��

� ��

� �� ∑ ∑ ��∗�,��

��

��

(3.7)

or

BnT = (B2nT)-1/2B1nT (3.8)

where BnT is Breitung t-statistic and ��2 is a consistent estimator of σ2.

Maddala and Wu (1999) and Choi (2001)

An alternative approach to panel unit root tests uses Fisher’s (1932) results to derive

tests that combine the p-values from individual unit root tests. The ADF and PP Fisher

panel unit root tests proposed by Maddala and Wu (1999) and Choi (2001) combine the

p-values of the test statistics for a unit root in each cross-sectional unit. The p-values are

computed from the ADF test (Maddala and Wu, 1999) and the PP test (Choi, 2001). The

simplicity of this test, its robustness to the choice of lag length and sample size and the

advantage of allowing for as much heterogeneity across units as possible make its use

attractive.

Choi (2001) considers the following model using the properties of Fisher test:

yit = dit + xit (3.9)

The observed data (yit) comprises of two components, namely a non-stochastic

component (dit) and a stochastic component (xit):

dit = -2∑ �� (3.10)

and

xit = ixi,t-1 + µit (3.11)

where µit is integrated of order zero, I(0), and may be heteroskedastic. Thus, the null

hypothesis is i = 1 for all i which implies unit root nonstationarity. The alternative

hypothesis for finite N is i< 1 for at least one i for finite N.

30

GiTi is a one-sided unit root test statistic (ADF or PP) for ith unit in the model following

assumptions hold:

under the null, as Ti ⟶ ∞, GiTi ⟶ Gi, (Gi being a non-degenerate random

variable)

µit is independent of µjs for all t and s when i≠j

��

� ⟶k as N ⟶ ∞, (k being a fixed constant).

Let pi be the p-value of a unit root test for a cross-sectional unit i, such that pi = �(��)

where F(.) is the distribution function of Gi. The proposed inverse chi-squared Fisher-

type panel unit root test statistic has the form:

P = -2∑ (��)�� . (3.12)

It combines the p-values from unit root tests for each cross-sectional unit i to test for

unit root in the panel. Under the null hypothesis of unit root, P is distributed as χ2(2N)

as Ti ⟶ ∞ for all N.

In this study we employ Fisher-type tests based on augmented Dickey-Fuller (Fisher-

ADF) and Phillips-Perron (Fisher-PP) Chi-square unit root tests. Using Fisher-type tests

are advantageous since they do not require balanced panels. Other advantages are the

availability for completely heterogeneous specifications and possibility to use different

lag lengths in the individual ADF or PP regressions. Additionally, Maddala and Wu

(1999) compare Fisher-type, IPS (2003) and LLC (2002) panel unit root tests and

present Monte-Carlo simulations as evidence in favor of Fisher-type tests in case of

cross-sectional correlation among variables. They also point out that when a mixture of

stationary and non-stationary series in the group is included in the alternative

hypothesis, the Fisher-types are the best among others because they are more powerful

in distinguishing the null and the alternative hypotheses.

3.1.2 Second generation panel unit root tests (cross-country dependence)

These so-called first-generation panel unit root tests are examples where the cross-

sectional dimension is used to construct tests that have higher power than individual

unit root tests. However, all the first-generation tests rely on independence along the

cross-sectional dimension. The first-generation panel unit root tests focus on panels

where the idiosyncratic errors were cross-sectionally uncorrelated. It was soon realized

that cross-sectional independence is a highly unrealistic assumption for most settings

encountered in practice, and it has been shown that the first-generation tests exhibit

31

large size distortions and low power in the presence of cross-sectional dependence (e.g.

O’Connell, 1998; Banerjee et al., 2004; Strauss and Yigit, 2003). Therefore, so called

second-generation panel unit root tests have been constructed to take the cross-sectional

dependence into account. These second-generation tests assume specific forms of the

cross-sectional dependence as their application depends on modelling the structure of

the dependence. As argued by Quah (1994), the modelling of cross-sectional

dependencies is a difficult task since no natural ordering exists in unit observations.

This is why various tests have been proposed including the works of Bai and Ng (2002),

Phillips and Sul (2003), Moon and Perron (2004), Choi (2002), Ploberger and Phillips

(2002), Moon, Perron and Phillips (2005), Chang (2002) and Pesaran (2003). Two main

approaches can be distinguished. The first one relies on the factor structure approach

and includes the contributions of Bai and Ng (2001), Phillips and Sul (2003), Moon and

Perron (2004), Choi (2002) and Pesaran (2003). The second approach consists in

imposing few or none restrictions on the residuals covariance matrix. This approach has

been adopted by Chang (2002) among others, who proposed the use of instrumental

variables in order to solve the nuisance parameter problem due to cross-sectional

dependency.

Pesaran (2003)

Pesaran (2003) considered a one-factor model with heterogeneous loading factors for

residuals. However, instead of basing the unit root tests on deviations from the

estimated common factors, he augmented the standard Dickey-Fuller or Augmented

Dickey-Fuller regressions with the cross-section average of lagged levels and first-

differences of the individual series. If residuals are not serially correlated, the regression

used for the ith cross-sectional unit is defined as:

∆yi,t = αi + iyi,t-1 + ci�� + di∆��t + vi,t (3.13)

where ��t-1 = 1/N � ��,��

�� and ∆�� = 1/N � �∆��,��

�

��. The Pesaran’s test is

based on these individual cross-sectionally augmented ADF statistics, denoted CADF. A

truncated version, denoted CADF*, is also considered to avoid undue influence of

extreme outcomes that may arise in small time T dimensions. In both cases, the idea is

to build a modified version of IPS t-bar test based on the average of individual CADF or

CADF* statistics (respectively denoted CIPS and CIPS*, for cross-sectionally

augmented IPS).

CIPS = �

�∑ ��

�� (�, �), (3.14)

CIPS* = �

�∑ ��

∗�� (�, �) (3.15)

32

where ti(N,T) denotes the t-statistic of the OLS estimate of i. The truncated CADF

statistic is defined as:

K1 if ti(N,T) ≤ K1

��∗(�, �) = ti(N,T) if K1 < ti(N,T) < K2 (3.16)

K2 if ti(N,T) ≥ K2

The constants K1 and K2 are fixed such that the probability that ti(N,T) belongs to

[K1,K2] is near to one. All the individual CADF (or CADF*) statistics have similar

asymptotic null distributions which don’t depend on the factor loadings. However, they

are correlated due to the dependence on the common factor. It is possible to build an

average of individual CADF statistics, although standard central limit theorems don’t

apply to these CIPS or CIPS* statistics. Pesaran showed that, the null asymptotic

distribution of the truncated version of the CIPS statistic exists and is free of nuisance

parameter. He proposed simulated critical values of CIPS and CIPS* for various sample

sizes. The critical values for CIPS for various deterministic terms are tabulated by

Pesaran (2007).

3.1.3 Panel unit root tests allowing structural breaks

The major weakness of the traditional unit root test is the failure to reject the unit root

hypothesis if the series has a structural break. This implies that series that are found to

be I(1) may in fact be stationary around the structural break; that is, I(0) but mistakenly

classified as I(1). Perron (1989, 1990) shows that failure to allow for break leads to a

bias that reduces the ability to reject a false unit root hypothesis. To overcome this

problem, Perron proposed allowing for a known or exogenous structural break in the

Augmented Dickey-Fuller (ADF) tests. Based on the short coming of this approach,

Zivot and Andrews (1992) and Perron (1997) propose determining the break point

endogenously. Lumsdaine and Papell (1997) extended the Zivot and Andrews (1992)

model to accommodate two structural breaks. However, Lee and Strazicich (2003)

criticized the endogenous break point tests for their treatment of breaks under the null

hypothesis. Lee and Strazicich (2003) propose a two break minimum Lagrange

Multiplier (LM) unit root test for structural breaks both under the null and the

alternative hypothesis that do not suffer from the spurious rejection of the null

hypothesis

Im, Lee and Tislau (2005, 2010)

33

The latest direction has been merged recently in the development of panel unit root tests

that allow for the presence of structural breaks and cross-sectional dependence. Im, Lee

and Tislau (2005) extended the univariate Lagrange Multiplier (LM) unit root test of

Lee and Strazicich’s (2003) to a panel LM test (ILT). Im et al. (2010) made use of a

simple transformation in order to obtain a Lagrange Multiplier (LM) panel unit test

statistic which is invariant to both the location and the size of breaks in the level or

trend of the series in the panel. This test depends only on the number of breaks in the

series and, therefore, has significantly greater power than all previous panel tests. In

addition, the test corrects for the presence of cross-correlations in the innovations of the

panel by applying the cross-sectionally augmented procedure of Pesaran (2007) that is

found to perform robustly under various specifications of cross-sectional dependence

(Baltagi et al., 2007).

The LM test of Lee and Strazicich (2004) which is obtained using the following

regression:

∆�� = ��∆�� + �� + ∑ ��

�� + ��, (3.17)

where Zt is the vector describing the breaks. Specifically, the case of one structural

change in the mean is formed as Zt = [1,t,Dt ]’, where Dt = 1 for t ≥ TB+1, and zero

otherwise. TB is the time period of the structural break. The LM t-test statistic is given

by the �̃, the t-statistic for the null hypothesis ϕ = 0. The location of the break is

determined endogenously by utilizing a grid search over all possible break points. Lee

and Strazicich (2004) provide simulated critical values of the minimum LM unit root

test.

Following Im et al. (2005), the test statistic (ILT) is based on the panel framework of the

equation (3.18) by implementing the testing regression on each cross-section unit:

Δ�� = ��Δ�� + ��,�� + ∑ ��

�� Δ��,�� + ��,�, (3.18)

where i is the cross-section unit, i = 1,2,…,N and t is the time period, t = 1,2,…,T. The

test statistic is based on the null hypothesis ϕi = 0 for all i, against the alternative

hypothesis ϕi < 0 for some i. The panel LM statistic can be constructed as the average of

univariate LM unit root t-test statistic estimated for each individual i:

��̅�� =

�

�∑ �̃��

�� (3.19)

The standardized panel LM unit root test statistic ILT is:

34

Γ�� =

√�(�̅�� (��))

��(��), (3.20)

where �(�̃��) and �(�̃��) are the expected value and variance of the individual �̃��

statistic, respectively. Thus, as N and T → ∞ as long as �(�̃��) and �(�̃��) exist and

N/T → k, we have �� → N(0,1) and the asymptotic distribution is not affected by the

presence of structural breaks.

The panel LM test of Im et al. (2005) will critically depend on the nuisance parameters

indicating the size and location of breaks when the series under investigation exhibits

breaks in both the intercept and the slope, and thus can be subject to serious size

distortions. To address this problem, Im, Lee and Tislau (2010) propose a new Lagrange

multiplier (ILT*) panel unit root test that is invariant to the nuisance parameters.

Following Lee and Strazicich (2009) the dependency of the test statistic on the nuisance

parameter can be removed by defining

��∗ = �

�

��, for � < ��

�

��, for �� < � < �

(3.21)

Using the transformed series, Im et al. (2010) formulate a test equation similarly to

equation (3.18) by replacing ��,�� with ��,��∗ . The transformed panel LM statistic can

be obtained as the standardized statistic of the following average test statistic:

��̅� = �

�∑ �̃��

∗�� (3.22)

Formally, the standardized panel LM unit root test statistic ILT* is obtained as:

��∗ = √�(�̅��(�̅��))

��(�̅��), (3.23)

where ��(��̅�) and ��(��̅�) are the estimated values of the average of the means and

variances of �̅ as reported in Table 2 of Im et al. (2010). The standardized LM panel unit

root test follows a standard normal distribution.

The previous panel LM unit root tests assumed no correlations in the innovations across

the panel. To correct for the presence of cross-sectional dependence, Im et al. (2010)

apply the cross-sectionally augmented procedure of Pesaran (2007) that is found to also

be robust to the presence of other sources of cross-section dependence such as the

spatial form (Baltagi et al., 2007). Therefore, they formulate the transformed testing

35

regression augmented by the cross-section averages of lagged levels and first-

differences of the individual series:

Δ�� = ��Δ�� + ��,��

∗ + ��̅��∗ + ℎΔ��̅

∗ + ∑ ��Δ��̅��∗�

�� + ∑ �� Δ��,�� + ��,

(3.24)

with ��̅��∗ = �� ∑ ��,��

∗�� and Δ��̅

∗ = �� ∑ Δ��∗�

�� = ��̅∗ − ��̅��

∗ . Therefore, the t-

statistic on ϕi is used in order to construct the mean statistic �̅ as in equation (3.22),

which in turn can be used to construct the ILT*CA test statistic equivalently to equation

(3.23), which again follows a standard normal distribution.

3.2 Cointegration tests

Using unit root tests we can verify the stationarity of the series. However, empirical

questions often concern multivariate relationships; it becomes essential to find out if

variables are cointegrated. In the time series framework, cointegration refers to the idea

that if a set of variables is individually integrated of order one, it is possible that some

linear combinations of these variables are stationary. In this case, the vector of slope

coefficients is referred to as the cointegrating vector.

Compared to panel unit root tests, the analysis of cointegration in panels is still at an

early stage of development although during the recent years there have been seen quite

big steps forward. So far, the focus of the panel cointegration literature has been on

residual-based approaches, although there have been a number of attempts to develop

system approaches as well. The residual-based tests were developed to ward against the

spurious regression problem that can arise in panels when dealing with I(1) variables.

Kao (1999) and Pedroni (1999, 2004) were among the first to propose residual-based

tests for the null hypothesis of no cointegration in cross-sectionally independent panels.

However, it has since then become clear that these tests do not work in general, as

cross-section dependence is likely to be the rule rather than the exception. In fact, as

Gengenbach, Palm, and Urbain (2006) showed the presence of unattended cross-section

dependence in the form of non-stationary common factors can actually cause the test

statics to diverge as N and T grows. As a response to this, they propose to estimate

separately the common and idiosyncratic components of Xit and Yit using the principal

components method of Bai and Ng (2004), and then to test for cointegration in the

resulting component estimates. Banerjee and Carrion-i Silvestre (2006), Westerlund

(2005, 2007) and Westerlund and Edgerton (2007) propose a similar test but instead of

applying the principal components method to Xit and Yit directly, they apply it to the

36

residuals of a first-stage regression of Yit onto Xit. Cointegration requires that both the

common and idiosyncratic components of the residuals are stationary.

With confirmation on the integrated order of variables of interest, we look for a

relationship between pork and beef prices using firstly the panel cointegration technique

developed by Pedroni (1999, 2004). This technique is a significant improvement over

conventional cointegration tests applied on a single country series. As explained in

Pedroni (1999), conventional cointegration tests usually suffer from unacceptable low

power when applied on data series of restricted length. The panel cointegration

technique addresses this issue by allowing one to pool information regarding common

long-run relationships between a set of variables from individual members of a panel.

Further, with no requirement for exogeneity of the regressors, it allows the short-run

dynamics, the fixed effects, and the cointegrating vectors of the long-run relationship to

vary across the members of the panel. Furthermore, it provides appropriate critical

values even for more complex multivariate regressions. Pedroni (2004) considers a set

of residual-based test statistics for the null of no cointegration in the general case of

fully endogenous regressors, no pooled slope coefficients and varying dynamics. The

advantage of these tests is that they pool only the information regarding the possible

existence of the cointegrating relationship that comes from the statistical properties of

the estimated residuals.

Pedroni (2004)

The implementation of Pedroni’s cointegration test requires estimating first the

following long run relationship:

yit = αi + δit + β1ix1it + … + βMixMit + εit, i = 1,…,N, t = 1,…,T and m=1,…,M

(3.25)

where N is the number of cross-sectional members in the panel; T is the number of

observation over time and M refers to the number of exogenous variables. The variables

yit and xit are assumed to be I(1), for each member i of the panel, and under the null of

no cointegration the residual εit will also be I(1). αi and δi are scalars denoting fixed

effects and unit-specific linear trend parameters, respectively and βi are the cointegra-

tion slopes. Both the slope coefficients β1i,…,βMi, and the member specific intercept αi

can vary across each cross-section.

For the computation of the panel test statistics we take the first-difference of the original

series and estimate the residuals of the following regression:

∆yit = β1i∆x1it + … + βMi∆xMit + πit (3.26)

37

Using the residuals from the differenced regression with a Newey-West (1987)

estimator the long run variance of �� is calculated. It is symbolized as �� :

�� =

�

�∑ ��,�

�� +

�

�∑ �1 −

�

��

�� ∑ ��,�

�� ,��. (3.27)

Pedroni considers the use of seven residual-based panel cointegration statistics for this

test. They are panel v -statistic, panel PP t -statistic, panel PP -statistic, Panel ADF t-

statistic, group PP t -statistic, group PP -statistic and group ADF t -statistic. The first

four statistics known as panel cointegration statistics are based on the within approach.

The last three statistics are group panel cointegration statistics and are based on the

between approach. The four within–dimension statistics are based on pooling the

autoregressive coefficients across the different cross-sectional units for the unit root

tests on the estimated residuals, whereas the three between-dimension statistics are

based on estimators that simply average the individual estimated coefficients for each

unit. Between-dimension-based statistics are therefore just the group mean approach

extensions of the within-dimension-based ones. In the presence of cointegrating

relationship, the residuals are expected to be stationary. The panel v- test is a one sided

test with the null of no cointegration being rejected when the test has a large positive

value. The other statistics reject the null hypothesis of no cointegration when they have

large negative.

Another distinction between the two sets of test is based on the alternative hypothesis

specification. Both sets of test verify the null hypothesis of no cointegration: ρi = 1 for

all i, where ρi is the autoregressive coefficient of estimated residuals under the

alternative hypothesis (�̂it = �� ̂it-1 + uit).

Alternative hypothesis specification is, however, different. The panel cointegration

statistics impose a common coefficient under the alternative hypothesis: ρi = ρ <1 for

all i. The group mean cointegration statistics allow for heterogeneous coefficients under

the alternative hypothesis and it results: ρi <1 for all i.

It is evident that the tests based on the between dimension are more general allowing for

cross-section heterogeneity.

Defining �̂it the estimated residuals from (3.25), the seven Pedroni’s statistics are:

�� =�

∑ ∑ �� ̂�,��

��

��

(3.28)

38

�� =∑ ∑ ��

� �� ̂�,�� ∆�̂��

��

∑ ∑ �� ̂�,��

��

��

(3.29)

�� =∑ ∑ ��

� �� ̂�,�� ∆�̂��

��

�� ∑ ∑ ��

� � �̂�,��

��

(3.30)

��∗ =

∑ ∑ ��

�� ̂�,�� ∆�̂��

��̃��∗� ∑ ∑ ��

� � �̂�,��

��

(3.31)

�̃�� = ∑∑ ��̂�� ∆�̂��

��

�∑ �̂�,��

��

�� (3.32)

�̃�� = ∑∑ ��̂�� ∆�̂��

��

��∑ �̂�,��

��

�� (3.33)

��∗ = ∑

∑ �̂�,�� ∗ ∆�̂��

∗��

�∑ �̃�∗��̂�,��

∗��

�� (3.34)

Kao (1999)

Kao (1999) was the first author to suggest the test for cointegration in homogeneous

panels. The Kao test statistics are calculated by pooling all the residuals of all cross-

sections in the panel; it is assumed that all the cointegrating vectors in every cross-

section are identical.

Kao considered the following system of cointegrated regressions in the homogeneous

panels:

xit = xi,t−1 + it (3.35)

yit = yi,t−1 + vit (3.36)

Let’s consider the regression:

yit = αi + βxit + uit, i = 1,…,N and t = 1,…,T (3.36)

where αi are individual constant terms, β is the slope parameter, it and vit are stationary

disturbance terms and so yit and xit are integrated processes of order 1 for all i.

The zero mean vector ξit = (vit, it)’ satisfies

�

√�∑ ��

|��|�� ⇒ ��(Ω) (3.37)

39

for all i as T ⟶ ∞, where Bi(Ω) is a vector of Brownian motion with asymptotic

covariance Ω. Kao derives two types of panel cointegration tests based on residuals

from panel least-squares dummy variable (LSDV) estimation.

The first is of a Dickey-Fuller (DF) type, which can be applied to the residuals using:

�� = ��,�� + eit (3.38)

The OLS estimate of is:

�� =∑ ∑ ��,��

��

��

∑ ∑ ��,��

��

(3.39)

The null hypothesis that = 1 is tested by:

√��(�� − 1) =

�

√��

�

∑�

�∑ ��,��

��

��

∑�

�� ∑ ��,��

��

(3.40)

Totally, Kao (1999) proposed four DF-type panel cointegration tests based on the OLS

residuals from the homogeneous panel regression. The first two DF statistics are based

on assuming strict exogeneity of the regressors with respect to the errors in the equation,

while the remaining two DF statistics allow for endogeneity of the regressors.

Kao also uses ADF-type panel cointegration test for cointegation in heterogeneous

panel. In this test version, the parameters are allowed to differ across the cross-sections.

Kao’s (ADF) type test can be calculated from:

�� = ��,�� + ∑ ��

�� Δ��,�� + �� (3.41)

where p is chosen so that the residuals eitp are serially uncorrelated. The ADF test

statistic is the usual t-statistic with = 1 in the ADF equation (Kao, 1999). The null

hypotheses is = 1 and the alternative hypotheses < 1. The asymptotic distributions

of the test converge to a standard normal distribution N(0,1) as T → ∞ and N → ∞.

Residual based (“first generation”) panel cointegration tests are often based on the

assumption of independent panel units. Breitung and Pesaran (2005) note that time

series are contemporaneously correlated in many macroeconomic applications using

country or regional data. Cross-sectional dependence can arise due to a variety of

factors, such as omitted observed common factors, spatial spillover effects, unobserved

common factors, or general residual interdependence, all of which could remain even

40

when all observed and unobserved common effects have been taken into account. The

literature on how to model cross-sectional dependence in large panels is still

developing.

Residual based panel cointegration tests result in low power for small samples. In the

presence of cross section dependencies, the tests are subject to large size distortions.

The situation gets worse if the number of cross sections is increased. To overcome these

deficits, panel cointegration tests have been developed that control for the dependencies

via a common factor structure (Banerjee and Carrion-i-Silvestre, 2006). The existence

of panel cointegration can be taken into account by the second generation panel

cointegration tests among of which we employ Westerlund (2007) and Westerlund and

Edgerton (2007), which seems to be more powerful when compared with the residual-

based panel cointegration tests.

Westerlund (2007) and Westerlund and Edgerton (2007)

We performed panel cointegration tests by Westerlund (2007), which are based on

structural rather than residual dynamics. Tests are based on error correction model

(ECM). These structural kind of test does not impose any common factor restriction,

which is a main reason associated to loss of power for residual-based cointegration tests.

The tests are based on the estimation of the following error correction equation:

Δyit = δ’idt +α(yi,t-1 – �’xi,t-1) +∑ �� ijΔyi,t-j +∑ ��

�� ∆xi,t-j + εit, i = 1,…,N and t =

1,…,T (3.42)

where yit is the dependent variable, xit is a vector of independent variables, dt =(1,t)’ is

the set of deterministic components and Δ is the first difference operator. Equation can

be rewritten as:

Δyit = δ’idt +αyi,t-1 + ��xi,t-1 +∑ �

�� ijΔyi,t-j +∑ ��

�� ∆xi,t-j + εit, (3.43)

where λi = -αi��. The parameter αi determines the speed at which the system Yi,t-1 –

��Xi,t-1 corrects back to the equilibrium after a shock.

Westerlund (2007) states that if αi < 0, then there is error correction, which implies that

yit and xit are cointegrated, whereas if αi = 0, there is no error correction and no

cointegration. It is indicated that the tests have limiting normal distributions and that

they are consistent.

41

Westerlund proposes 4 panel cointegration tests. Pα and Pτ are panel statistics which are

based on pooling the information regarding the error correction along the cross sectional

units. The null hypotheses for the panel tests is αi = 0 and alternative hypotheses is αi =

α < 0 for all i. Gα and Gτ are group statistics which do no exploit the information

regarding the error correction. The null hypotheses for the group tests is αi = 0 and

alternative hypotheses is αi < 0 for at least some i. The test proposed by Westerlund

(2007) does not only allow for various forms of heterogeneity, but also provides p-

values which are robust against cross-sectional dependencies via bootstrapping.

The test developed by Westerlund and Edgerton (2007) relies on the popular Lagrange

multiplier test of McCoskey and Kao (1998), and permits heteroskedastic and serially

correlated errors, unit specific time trends and cross-sectional dependence. It also allows

an unknown structural break in both the intercept and slope of the cointegrated

regression, which may be located different dates for different units. In addition, this

bootstrap test is based on the sieve-sampling scheme, and has the advantage of

significantly reducing the distortions of the asymptotic test. Hence, the test also works

well in the small sample analysis. Another appealing advantage is that here the null

hypothesis is now cointegration which implies, if not rejected, the existence of a long-

run relationship for all panel members. The alternative hypothesis is that there is no

cointegrating relationship for at least one country of the panel.

Johansen’s Fisher panel cointegration test (Maddala and Wu, 1999)

The last cointegration test we perform is Johansen-Fisher based panel cointegration test

suggested by Maddala and Wu (1999). Johansen’s methodology takes its starting point

in the vector autoregression (VAR) of order p given by

�� = � + �� + ⋯ + �� + ��, (3.44)

where �� is an n×1 vector of variables that are integrated of order one, I(1), and �� is an

n×1 vector of innovations. This VAR can be re-written as

∆�� = � + �� + ∑ �� Δ�� + �� (3.45)

where

� = � ��

�

��

− � and �� = − � ��

�

��

If the coefficient matrix Π has reduced rank r<n, then there exist n×r matrices α and β

each rank r such that Π = αβ′ and β′yt is stationary. r is the number of cointegrating

42

relationships, the elements of α are known as the adjustment parameters in the vector

error correction model and each column of β is a cointegrating vector. It can be shown

that for a given r, the maximum likelihood estimator of β defines the combination of yt-1

that yields the r largest canonical correlations of Δ�� with �� after correcting for

lagged differences and deterministic variables when present. Johansen proposes two

different likelihood ratio tests of the significance of these canonical correlations and

thereby the reduced rank of the Π matrix: the trace test and maximum eigenvalue test,

shown in equations (3.46) and (3.47) respectively.

�� = −� ∑ ln (1 − �� ) (3.46)

�� = −�ln(1 − ��) (3.47)

Here T is the sample size and �� is the ith largest canonical correlation. The trace test

tests the null hypothesis of r cointegrating vectors against the alternative hypothesis of n

cointegrating vectors. The maximum eigenvalue test, on the other hand, tests the null

hypothesis of r cointegrating vectors against the alternative hypothesis of r+1

cointegrating vectors.

Using Johansen’s (1988) test for cointegration, Maddala and Wu (1999) consider

Fisher’s (1932) suggestion to combine individuals tests, to propose an alternative to the

two previous tests, for testing for cointegration in the full panel by combining individual

cross-sections tests for cointegration.

If πi is the p-value from an individual cointegration test for cross-section i, then under

the null hypothesis for the whole panel,

−2 ∑ log�(��

�� ) (3.48)

is distributed as �� .

43

3.3 Estimation methods

It is well know that the panel cointegration relationship provides evidence in favor of

the existence of causal relationships but it does not indicate the direction of the causal

linkages between variables. Recently, econometricians have begun to modify Granger-

causality11 tests to incorporate panel dynamics (see for example Arellano and Bond

(1991), Holtz-Eakin et al. (1988), Hurlin (2005) and Hurlin and Venet (2001)). Within

panel frameworks, Granger-causality tests generate meaningful results with significant-

ly shorter time, incorporate significantly more observations, and produce more efficient

results than Granger-causality tests in the conventional context (Hurlin and Venet,

2001).

The recent literature highlights four approaches that could be implemented to test for

Granger-causality in panel models. The first one, based on the random or fixed effects

or on the Generalized Method of Moments (GMM), is usually employed to estimate

homogenous panel model by eliminating the fixed effect. The main disadvantage of this

method is that it assumes that slope parameters are similar which may lead to

inconsistent and misleading long-term coefficients (Pesaran et al., 1999). Furthermore,

this method does not allow for heterogeneity and cross-sectional dependence among

individuals. The second approach to test Granger causality, initiated by Hurlin (2007)

takes into account the heterogeneity across units but pay no attention to the

possible cross-sectional ependence (i.e., existence of common shocks). The third

approach proposed by Konya (2006), deals with both heterogeneity and cross-sectional

dependence across units and allows for testing Granger-causality on each individual

panel member separately by taking into account simultaneously contemporaneous

correlation across units. The forth approach, launched by Pesaran and Smith (1995)

proposes the Mean Group estimator (MG) for testing Granger causality. Constructed

separately for each group, the model computes a simple arithmetic average of the

coefficients consenting to the intercepts, short-run coefficients and error variances to

differ across groups. According to Pesaran and Smith (1995), this estimator provides

consistent estimates of the parameters’ averages. Pirotte (1999) also shows that the

mean group estimator provides efficient long-run estimators for a large sample size. It

allows the parameters to be freely independent across groups and does not consider

potential homogeneity between groups. Finally, the forth approach developed by

Pesaran, Shin and Smith (1999), focuses as well on non-stationary dynamic panels with

11 The term of Granger-causality, proposed by Granger (1969), is not a true causality concept but a statistical tool which in principle concerns only the predictability between time-series variables. It could be understood as following: X is said to “Granger cause” Y if and only if Y is better predicted by using the past values of X than by not doing so. Although not a real causality identification, Granger-causality analysis is widely applied in business forecast and policy-modeling.

44

heterogeneous parameters. It offers an intermediate estimator, the Pooled Mean Group

estimator (PMG) which allows the intercept, short-run coefficients and error variances

to be different across the groups but constraints the long-run coefficients to be equal

across these groups.

We are interested to detect short-run and long-run causal relationships between pork and

beef prices among the EU member states by implementing the MG and the PMG

estimators.

Mean Group and Pooled Mean Group estimators

The original Pesaran, Shin and Smith (1999) paper starts with an autoregressive

distributed lag (ARDL) dynamic panel specification, with p being the number of lags

for the dependent variable, and q the number of lags for the explanatory variables. The

specification takes the form:

yit = ∑ ��,�� + ∑ ��

�� ,�� + �� + ��

�� , I = 1,2,…,N and t = 1,2,…,T

(3.49)

where λij are coefficients of the lagged dependent variable, Xit is a set of regressors and

δij is a (k×1) vector of its coefficients. Group-specific effects are represented by μi while

it is the error term. This model specification requires that T is large enough, which

means that there is a reason to expect that some variables might not be stationary. In

case the variables are integrated of order one, and consequently cointegrated, then we

expect that the error term is stationary for all i. Typically, cointegrated variables react to

deviations from their long-run equilibrium, and adjust in the short-run. These sorts of

deviations and reactions are usually presented as error-correction models by reparame-

terizing (3.49):

Δyit = ��(��,�� + θ�

��) + ∑ ��∗ ∆��,�� + ∑ ��

∗�� ∆��,�� + �� + ��

�� (3.50)

where

�� = − �1 − � ��

�

��

� , θ� = ∑ ��

��

1 − ∑ ��, ��

∗ = − � ��,

�

��

� = 1,2, … , � − 1 and ��∗ = − � ��

�

��

45

The error coefficient, or the speed of adjustment term, is presented here as ϕi. In case ϕi

is statistically significant, there is evidence of a long-run relationship, while a negative

ϕi implies that the variables return to the equilibrium after a deviation in the short-run.

�� is the vector of long-run coefficients, while ��

∗ and ��∗ are short-run coefficients of

the lagged dependent and explanatory variables, respectively.

Pesaran et al. (1999) recommend using maximum likelihood (ML) estimation method

for estimating equation (3.50), as it is nonlinear in its parameters. However, prior to ML

estimation, we must make a few assumptions about this specification. Firstly, equation

(3.50) is rewritten by stacking the time-series observations for each group:

Δyi = ��(��,�� − ��) + ∑ ��∗ ∆��,�� + ∑ ��

∗�� ∆��,�� + �� + ��

�� (3.51)

where � = (1,...,1)’ is a (T×1) vector of ones, and the disturbance term is i = (i1,…,iT)’.

The error terms are distributed independently across groups, time, and of the regressors.

The last assumption is necessary for a consistent estimate of short-run coefficients, as

we allow them to differ across groups. To control for the long-run relationship, and for

the adjustment to the long-run equilibrium, the speed of error-correcting adjustment

term, ϕi, is negative. This is ensured when the model given by equation (3.50) is stable in its roots that lie outside the unit circle, or that ∑ ��

�� = 1. Now there exists a

long-run relationship between the dependent variable and regressors, with �i the long-

run coefficients and it a stationary process:

�� = −�� + ��. (3.52)

Once we confirm there is a long-run relationship, we can rewrite its coefficients without

the group-specific term: �� = �.

We rearrange equation (3.51):

�� = ��(�) + �� + ��, (3.53)

where ��(�) = ��,�� − ��, �� = �Δ��,��, … , Δ��,��, Δ��, Δ��,��, … , Δ��,��, ��

and �� = ��,�∗ , … , ��,��

∗ , ��,�∗�

, ��,�∗�

, … , ��,��∗�

, ��.

There are no restrictions on the short-run coefficients. The error variances are allowed

to differ across groups, var(it) = ��. The nonlinearity of the parameters � and ϕi

implies that a likelihood approach should be used in panel estimation. Just for these

purposes, it is assumed that the error term is normally distributed. It is possible to

express the likelihood of the panel as the product of each, separate, group-specific

likelihoods:

46

��(�) = �

2� ln(2��

�)

�

��

−1

2�

1

��

�

��

[Δ�� − ��(�)]��[Δ�� − ��(�)] (3.54)

where

�� = �� − ��(��

��)��

�, � = (��, ��, ��)�, � = (��, ��, … , ��)� and �

= (��, ��

�, … , ��)�

In order to get consistent and asymptotically normal estimators, it is necessary to add

some further assumptions that can be found in the original paper (Pesaran, 1999, p. 624-

625).

Maximizing equation (3.54) with respect to φ gives us estimates of the long-run

coefficients �� and of the error-correction coefficients ��:

�� = − ��

�

��

�

��

��

��

× ��

��

�

��

��Δ�� − ��,��,

�� = ��

��

��Δ�� (3.55)

where �� = �� = ��,�� − ��. Estimators in (3.55) are called Pooled Mean Group

(PMG) estimators because they reflect both pooling, inherent in ��, and averaging across

groups, inherent in ��. Solving the maximization problem provides the error variance

estimate as well:

�� = ��Δ�� − ��

��Δ�� − ��. (3.56)

The PMG estimators are computed using the algorithm of back-substitution or in other

words, by iterating the obtained estimates. Using the initial estimate ��, one can get

�� and �� from equations (3.55) and (3.56), respectively. These estimates can then be

replaced into equation (3.54) to get a new estimate of θ, used to get new estimates of φ

and ��, repeated until convergence is achieved. It should be noted that the ML estimates

of long-run and short-run parameters are asymptotically distributed independently of

each other, implying that once we get the long-run parameters, we can use them to

consistently estimate the short-run and the error-correction coefficients.

Indeed, the PMG estimator allows to evaluate two different Granger-causality

relationships: a short-run causality, testing the significance of the coefficients related to

the lagged difference of variables (H0: δi = �� = 0 for all i) and a long-run causality

47

related to the coefficient of ECT term (H0: ϕ = 0 for all i). The parameter ϕ represents

the speed of adjustment at which the values of variables come back to the long-run

equilibrium levels, once they deviate from the long-run equilibrium relationship. The

negative sign of the estimated speed of adjustment coefficients are in accordance with

the convergence toward the long-run equilibrium. The larger the value of ϕi, the stronger

is the response of the variable to the deviation from the long run equilibrium. On the

flip-side, in the case of low coefficients values, any deviation from long-run equilibrium

requires much longer time to force the variables back to the long-run equilibrium.

Although the parameters obtained by iteration are identical to those obtained from full-

information maximum likelihood, the covariance matrix is not. Nevertheless, we can

recover the covariance matrix for all estimated parameters, because we know the

distribution of the PMG parameters. The covariance matrix can be estimated by the

inverse of:

⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎡�

��

��

��

�

��

−��

� ��

�� … −

��

�� −

��

�� … −

��

��

��

�� … 0

��

�� … 0

⋱ ⋮ ⋮ ⋱ ⋮��

� ��

�� 0 …

��

��

��

�� … 0

⋱ ⋮��

� ��

�� ⎦

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎤

With the mean group (MG) estimator proposed by Pesaran and Smith (1995), the

intercepts, slope coefficients, and error variances are all allowed to differ across groups.

MG estimator runs ARDL equations separately and calculates the mean of the short and

long-run parameters across groups by the simple arithmetic average of the unit specific

coefficients. The MG estimator assumes that all of the parameters can differ across units

but does not allow for long-run homogeneity. Though MG estimator can provide

consistent estimates, it depends on quite strong assumptions and does not take into

account that certain parameters may be the same across groups. The MG parameters are

then simply unweighted means of individual coefficients, or:

��= N-1∑ �� (3.57)

48

with the variance

�� = �

�(��)∑ (��

�� − ��)� (3.58)

It is possible to test for the suitability of the PMG estimator vs. the MG estimator based

on the consistency and efficiency properties of the two estimators, using a likelihood

ration test or a Hausman test. If the restrictions imposed on the long-run coefficients are

valid then the PMG is more efficient than the MG estimator, while it becomes

inconsistent if the restrictions do not apply. Rejection of the test would suggest that the

sample is too heterogeneous to be pooled.

It is worth noting that obtaining consistent and efficient PMG estimator requires several

conditions to be satisfied. First, the time dimension has to be long enough for the

estimation of the model for each cross-sections separately. Second, the lag order must

be chosen to ensure that the residuals of the error correction model are serially

uncorrelated, but it should not cause loss of degrees of freedom as well. Third, PMG

assumes cross-sectional independence of the regression residuals εit. Arising from

omitted common effects (e.g. time-specific effects or common shocks affecting

countries), cross-sectional dependence influences ARDL process and causes misspecifi-

cation. Pesaran et al. (1999) offer either to use cross-sectional means of the existent

regressors as additional regressors or include all of the variables as deviations from their

respective cross-sectional means in each period. The fourth condition is the existence of

long-run relationship between the variables and it requires a negative and significant

error correction term (φi). Finally, PMG estimator is both consistent and efficient if and

only if the long-run parameters are homogenous across countries.

Dynamic OLS (DOLS) and Fully Modified Ordinary Least Squares (FMOLS)

To check the robustness of our results, we also apply another two heterogeneous

dynamic panel estimators, the Dynamic OLS (DOLS) and the Fully Modified Ordinary

Least Squares12 (FMOLS). The methods are asymptotically equivalent (Banerjee, 1999).

The PMG is based on the maximum likelihood estimation, while the DOLS and the

FMOLS, as their names suggest, are modified OLS estimators. The DOLS and the

FMOLS are popular in conventional time series econometrics, for they are believed to

eliminate endogeneity in the regressors and serial correlation in the errors.

12 The FMOLS is popular in conventional time series econometrics, for it is believed to eliminate endogeneity in the regressors and serial correlation in the errors.

49

The presence of cointegration and unit roots considerably affects the asymptotic

distributions in time series as well as in panel analysis. However, cointegration

equations have attractive properties: as the number of observations increase in T and N,

the OLS estimation of the cointegrated variables converges in the long-run equilibrium

to the true value. FMOLS and DOLS methodologies are proposed by Kao and Chiang

(2000) and Pedroni (2000) to estimate the long-run cointegration vector for non-

stationary panels. They found that while the OLS estimator is normal distributed with

non-zero mean, the fully modified FMOLS and dynamic DOLS estimators are

asymptotically normal with zero mean. The basic idea behind both the estimators is to

correct the standard pooled OLS for endogeneity bias and serial correlation that are

normally present in long-run relationship and thereby allow for standard normal

inference.

Let us consider the following fixed effect panel regression:

�� = �� + �� + ��, � = 1, … , �; � = 1, … , �, (3.59)

where yit is a matrix (1,1), β is a vector of slopes (k,1) dimension, α is individual fixed

effect, uit are the stationary disturbance terms. It is assumed that xit (k,1) vector are

integrated processes of order one for all i, where:

�� = ��,�� + �� (3.60)

A standard panel OLS estimator for the coefficient in equation is given by:

��,�� = �� (�� − �̅�)�

�

��

�

��

�

��

� �(�� − �̅�)

�

��

�

��

(�� − ��) (3.61)

Where �̅� and �� refer to the individual means for each i member of the cross section.

According to Pedroni (2000) this estimator is asymptotically biased and its distribution

is dependent on nuisance parameters associated with the dynamics underlying the

processes determining x and y. Only if x is strictly exogenous and the dynamics are

homogeneous across i members of the panel ��,�� is unbiased.

Pedroni (2000) suggested the group-mean FMOLS and DOLS estimators. According to

Pedroni group mean tests are preferred over the pooled tests since they allow greater

flexibility under alternative hypotheses. In our analysis we utilize group-mean versions

of FMOLS and DOLS by Perdoni (2000).

50

The test statistics derived from the group-mean estimators are constructed to test the

null hypothesis βi = β0 for all i against the alternative hypothesis βi ≠ β0, so that the

values for βi are not constrained to be the same under the alternative hypothesis.

Let’s define �� = (��, ��) ~ �(1) and �� = (�� + ��) ~ �(0) with long run

covariance matrix Ωi = �� (Li is a lower triangular decomposition of Ωi). In this case,

the variables are said to be cointegrated for each member of the panel, with cointegrat-

ing vector β. The terms αi allow the cointegrating relationship to include member

specific fixed effect. The covariance matrix can also be decomposed as

�� = ��

� + �� + ��, (3.62)

where �� is the contemporaneous covariance and is a weighted sum of autocovariances.

The Panel FMOLS estimator which is parametric approach for the coefficient β is

defined as follows:

��,�� = �� ∑ (∑ (�� − �̅�)��

�� )�� (∑ (�� − �̅�)

��

∗ − ��̂�) (3.63)

where

��∗ = (�� − ��) −

��

��∆��, �̂� = Γ�� + Ω��

� − ��

��Γ�� − Ω��

� � (3.64)

and Li is a lower triangular decomposition of Ωi defined as follows:

Ω� = ��

�

�� (3.65)

Pedroni (2001) also considers a group-mean DOLS estimator which is non-parametric

approach. This is done by modifying equation (3.64) and by including lead and lag

dynamics:

�� = �� + �� + ∑ ��

Δ��,�� + �� (3.66)

and the estimated coefficient β is given by:

��,�� = [�� ∑ (∑ ��

�� )��(∑ ��

∗ )�� ]� (3.67)

where �� = �� − �̅�, Δ��,��, … , Δ��,�� is the 2(K+1)×1 vector of regressors.

51

4 DATA DESCRIPTION

In order to analyze the price integration of pork and beef in the EU, we use the data on

monthly pork and beef prices in the EU member states, which are obtained from Tike

(Agricultural Statistics Finland)13. Tike is responsible for agricultural price monitoring

in Finland and price monitoring reports include data on both Finland and other EU

countries.

In our dataset, the EU member countries are categorized into three groups of EU1514,

EU2515 and EU2716. Data on EU15 covers the period from February 1995 to June 2014.

Correspondingly, the data on EU25 covers the period from January 2005 to June 2014

and the data on EU27 covers the period from January 2007 to June 2014. In other words

this means that the single price time series from EU15 includes 221 observations; the

single price time series from EU25 consists of 102 observations and the single price

time-series from EU27 includes 78 observations. In empirical analysis we carry out our

investigations by utilizing six different data specifications: we have one group of EU

member states (EU15 countries) when investigating period 2/1995-6/2014, we have two

groups of EU member states (EU15 and EU25) when investigating period 1/2005-

6/2014 and we have three groups of EU member states (EU15, EU25 and EU27) when

investigating period 1/2007-6/2014. By repeating the estimations with different group of

member states we can make conclusions regarding stability of the results over time and

also regarding cross-sectional stability.

We have constituted our monthly price data by taking averages from weekly data.

Weekly data includes many missing values which are problematic in our investigation.

We have estimated and filled some missing values with the help of data authors. As a

result, we have succeeded to create balanced monthly level panel data. However, one

should notice that Malta is missing in the panels because its time-series includes serious

13 I am grateful to Pia Outa and Lauri Juntti for sending me the excellent price data. 14 EU15 member states are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United Kingdom. 15 EU25 member states are EU15 plus Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic and Slovenia. 16 EU27 member states are EU25 plus Bulgaria and Romania.

52

shortages17. Regarding other countries, quality of data is good and time-series are

completed.

Carcass classification plays an important role in the European Union meat market. The

aim of the carcass classification scheme is to ensure a common classification standard

throughout the European Union which enables the EU to operate a standardized price

reporting system. The EU classification of the analyzed pork is labeled “E”, which

indicates that 55% or more of the carcass has to be lean meat. The quality of the beef is

correspondingly “R3”. According to the EU grading scheme, it is qualitatively good

meat, which means that the overall profiles are straight, the muscle development is good

and the content of fat is medium. These two are also the most typical carcass classes.

The descriptive statistics of pork and beef prices are reported for each member state in

Tables 4.1 and 4.2. Notice that in empirical analysis we use the natural logarithms of

these variables in order to obtain the elasticities.

17 Despite the fact that Malta is missing in our data sets, we use terms “EU25” and “EU27” in our empirical investigations.

53

Table 4.1. Descriptive statistics of pork prices in the EU member states

Mean Std. Min Max Mean Std. Min Max Mean Std. Min Max

Austria 148.5 21.8 86.2 227.1 151.6 15.7 122.7 195.3 152.6 16.9 122.7 195.3

Belgium 140.7 21.3 82.3 228.9 141.9 13.8 117.4 183.5 142.8 14.8 117.4 183.5

Bulgaria 179.0 14.8 153.9 230.7

Cyprus 173.7 24.1 126.9 234.0 172.2 24.9 126.9 234.0

Czech Republic 155.0 16.9 122.2 196.4 157.5 17.7 122.2 196.4

Denmark 131.8 19.4 88.2 195.9 134.4 16.5 107.0 176.0 136.9 17.4 107.0 176.0

Estonia 152.5 13.5 133.4 184.4 156.1 12.7 133.6 184.4

Finland 143.0 15.3 109.2 184.8 149.7 14.4 130.7 184.8 153.4 13.9 135.1 184.8

France 140.3 19.5 93.9 206.4 142.3 15.4 116.0 190.0 143.4 16.3 116.0 190.0

Germany 150.5 22.1 84.5 232.1 155.4 15.4 118.3 197.9 156.6 16.3 118.3 197.9

Greece 172.8 24.9 110.4 241.4 179.0 18.1 130.6 213.9 177.8 18.9 130.6 213.9

Hungary 153.6 15.9 123.3 193.1 156.1 16.6 126.3 193.1

Ireland 136.8 16.6 88.7 179.7 142.8 14.1 117.5 174.2 144.7 14.9 117.5 174.2

Italy 159.4 22.4 99.7 226.7 162.4 21.6 120.7 226.7 165.9 22.4 125.4 226.7

Latvia 160.9 17.3 129.4 203.4 163.8 18.1 129.4 203.4

Lithuania 156.7 16.9 126.4 206.9 160.1 17.0 126.5 206.9

Luxembourg 157.7 22.3 104.1 257.2 155.7 14.3 126.2 196.4 156.9 15.1 126.2 196.4

Netherlands 132.3 21.3 64.3 226.5 139.0 14.6 110.9 179.0 140.3 15.4 110.9 179.0

Poland 148.1 20.6 112.2 197.5 152.6 20.3 112.2 197.5

Portugal 154.6 22.9 85.3 223.5 160.0 16.4 129.0 201.0 161.6 16.4 129.0 201.0

Romania 164.7 17.1 136.7 205.6

Slovakia 157.5 17.7 124.4 201.8 160.7 18.1 124.4 201.8

Slovenia 150.4 14.8 118.6 191.4 151.0 16.2 118.6 191.4

Spain 150.0 24.8 74.2 217.1 157.4 20.9 118.1 217.1 159.7 21.7 118.1 217.1

Sweden 144.7 19.3 104.4 198.2 152.7 19.8 118.1 198.2 157.1 19.8 118.1 198.2

UK 155.1 19.6 89.9 201.5 163.2 16.8 127.8 201.5 167.1 16.7 127.8 201.5

PORK2/1995-6/2014 (EU15) 1/2005-6/2014 (EU25) 1/2007-6/2014 (EU27)

54

Table 4.2. Descriptive statistics of beef prices in the EU member states

The descriptive statistics reveal, among other things, that in the new member states pork

prices are higher than in the old member states. However, in the case of beef, the

situation is opposite. In figures 4.1, 4.2 and 4.3 are showed how the pork and beef prices

has evolved during the period 2/1995(EU15) or 1/2004(EU25) or 1/2007(EU27)-6/2014

in the EU member states. In order to make the figures more comparable, we have

indexed the data. From the user’s point of view, the price indexes are the most practical,

because index revisions do not interrupt the series. Additionally, visual inspection of the

pork and beef price time series, and in advance of the subsequent econometric analysis,

may help to assess sustainability issues in individual cases.

All in all, the figures confirm that there is some relationship between the pork and beef

prices. The relationship between the price series has strengthened since the beginning of

the 2000s in the EU15 member states. Before that, the developments in the pork and

beef price series were divergent. Notable, fluctuations in pork price series are much

larger than fluctuations in beef price series in the EU15. However, when examining the

price series of new member states the situation is opposite: beef prices have fluctuated

more than pork prices.

Mean Std. Min Max Mean Std. Min Max Mean Std. Min Max

Austria 303.4 41.4 215.0 410.0 335.8 34.9 243.3 410.0 343.6 35.1 243.3 410.0

Belgium 259.1 29.9 201.8 332.3 274.0 26.2 209.4 332.3 280.4 24.5 244.9 332.3

Bulgaria 242.2 44.8 163.6 360.5

Cyprus 286.5 22.9 235.5 345.0 293.3 20.5 251.7 345.0

Czech Republic 294.7 30.5 235.5 362.4 303.6 27.8 251.7 362.4

Denmark 301.2 49.3 219.2 413.8 340.7 39.6 259.3 413.8 350.7 37.5 277.4 413.8

Estonia 262.5 41.3 179.3 357.0 275.6 35.4 179.3 357.0

Finland 315.0 42.4 242.5 424.9 346.5 35.4 286.8 424.9 358.3 30.4 305.7 424.9

France 300.8 41.9 215.0 401.4 332.5 35.1 262.0 401.4 337.7 37.0 262.0 401.4

Germany 296.2 49.5 168.2 423.3 334.9 39.7 261.6 423.3 343.6 39.7 261.6 423.3

Greece 392.2 27.8 313.1 455.5 413.2 21.5 362.6 455.5 422.1 12.7 371.4 455.5

Hungary 242.9 19.0 185.1 296.0 239.4 19.5 185.1 296.0

Ireland 281.5 52.6 188.4 437.7 322.1 46.3 238.5 437.7 334.1 44.5 272.0 437.7

Italy 328.6 39.9 197.3 422.3 359.7 29.3 277.0 422.3 366.8 27.2 277.0 422.3

Latvia 207.1 39.4 127.9 321.0 217.9 36.8 147.0 321.0

Lithuania 250.8 46.1 164.3 335.2 261.3 45.5 178.5 335.2

Luxembourg 301.7 38.9 182.3 399.7 327.6 33.8 247.8 399.7 335.2 33.2 247.8 399.7

Netherlands 277.0 40.4 171.1 380.3 303.7 29.1 244.0 380.3 309.6 29.0 256.8 380.3

Poland 271.5 38.0 210.4 354.7 281.7 36.2 217.7 354.7

Portugal 324.0 31.4 247.2 390.7 347.0 21.5 283.9 390.7 351.6 17.4 308.4 390.7

Romania 249.4 40.4 159.9 346.1

Slovakia 291.5 41.4 182.4 390.7 302.5 39.5 216.5 390.7

Slovenia 314.4 32.8 254.1 392.0 323.2 31.0 258.7 392.0

Spain 305.4 40.6 208.1 400.7 336.7 32.4 279.0 400.7 343.7 31.7 283.9 400.7

Sweden 287.8 49.0 211.3 460.3 316.7 54.3 231.3 460.3 329.6 54.0 231.3 460.3

UK 292.4 55.9 195.3 450.9 330.5 56.6 238.7 450.9 345.2 54.4 276.8 450.9

BEEF2/1995-6/2014 (EU15) 1/2005-6/2014 (EU25) 1/2007-6/2014 (EU27)

55

The figures also reveal that there exist seasonal cycles particularly in pork prices and

mainly in the Southern European countries. In order to control for the potential seasonal

differences in our econometric investigations in the next chapter, we add dummy

variables for the months of January and March through December. For example, Osborn

(1993) argued that seasonal non-stationarity can often be adequately represented by

seasonal dummies.

Figure 4.1. The development of pork and beef prices in the EU15 member states

during the period 2/1995-6/2014, weekly data, index: week 5/1995=100.

50

100

150

200

50

100

150

200

50

100

150

200

50

100

150

200

50

100

150

200

1995 20142004 1995 20142004 1995 20142004

Austria Belgium Denmark

Finland France Germany

Greece Ireland Italy

Luxembourg Netherlands Portugal

Spain Sweden UK

Beef Pork

Year

Graphs by EU15 Member State

56

Figure 4.2. The development of pork and beef prices in the EU25 member states (excl.

EU15) during the period 1/2005-6/2014, weekly data, index: week 1/2005=100.

Figure 4.3. The development of pork and beef prices in the EU27 member states (excl.

EU25) during the period 1/2007-6/2014, weekly data, index: week 1/2007=100.

50

100

150

200

50

100

150

200

50

100

150

200

2005 20142010 2005 20142010 2005 20142010

Cyprus Czech Republic Estonia

Hungary Latvia Lithuania

Poland Slovenia Solvakia

Beef Pork

Year

Graphs by EU25 new Member States

50

100

150

200

2007 2010 2014 2007 2010 2014

Bulgaria Romania

Beef Pork

Year

Graphs by EU27 new Member States

57

5 EMPIRICAL RESULTS

5.1 Panel unit root results

Panel cointegration testing requires that variables are integrated in the same order. To

determine the level of integration of the variables, panel unit root tests have been carried

out. Thus, we start by presenting the results of five panel unit root tests discussed in

Section 3.1, namely, LLC, IPS, Breitung, ADF-Fisher and PP-Fisher tests. In this

application of these tests the dependence between the series has not been taken into

account. Table 5.1 show the statistics and the p-values from the panel unit root tests

concerning beef price series and Table 5.2 concerning pork price series. We use the

natural logarithms of the prices.

The first sections in Table 5.1 and 5.2 show the empirical result with price series that

include individual effects only, while the latter sections include individual effects and

individual trends. The trends amount to fixed effects in the first difference specification.

Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution,

χ2 with 2*N degrees of freedom. IPS, LLC and Breitung tests assume asymptotic

normality. Selection of lags is based on the Schwarz criterion (SIC)18 and the Newey-

West bandwidth19 selection using the Bartlett-Kernel20 method.

The alternative hypothesis of a stationary process differs between panel unit root tests.

Namely, the alternative hypothesis of LLC and Breitung tests assume that all panels are

stationary. In other tests the alternative hypotheses assume that some or at least one

cross section is stationary. The null hypothesis is the same in all the tests: all panels

contain unit roots.

18 Estimating the lag length of autoregressive process for a time series is a crucial econometric. When fitting models, it is possible to increase the likelihood by adding parameters, but doing so may result in overfitting. Schwarz criterion (SIC) (or Bayesian information criterion (BIC)) resolves this problem by introducing a penalty term for the number of parameters in the model. Thus, SIC is a criterion for model selection and it is based on maximizing or minimizing the likelihood function. 19 Because the PP test differs from the ADF test in the treatment of serial correlation, the information-based method (such as SIC) does not apply to the PP test. In the PP test, an issue concerns the choice of a lag truncation parameter is a choice of the bandwidth (autocovariance lag). Newey and West (1994) proposed a method for estimating the optimal bandwidth from truncated sample autocovariances. 20 A weighting function used in non-parametric estimation techniques.

58

It is also worthwhile to member that both LLC and IPS tests present important size

distortions when either N is small or N is relatively large with respect to T. In our case N

is relatively small with respect to T.

According to the results, more or less, a unit root is detected for the level variables,

while the first differences appear to be stationary. For the beef and pork price series,

only the LLC and Breitung tests reject the null in some cases, while other three tests can

not reject the unit root hypothesis. After first-differencing the variables and repeating

the tests, nonstationarity is eliminated as all panel unit root tests reject the null of

nonstationarity for the first-differenced variables at the 1% level of significance. We can

thereby conclude that according to first generation panel unit root tests first-differenced

variables are stationary so that panel variables are integrated of order one, I(1). These

results imply that the long-run panel variables are not stationary, and that these variables

could be cointegrated.

59

EU1

5EU

15

EU2

5EU

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5EU

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EU15

EU1

5EU

25EU

15

EU25

EU

27

2/1

99

5-6

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1/2

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5-6

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141

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1/2

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7-6

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141

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07-

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01

41

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-6/2

014

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5-6

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1/2

00

5-6

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14

1/2

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5-6

/20

14

1/2

00

7-6

/20

14

1/2

00

7-6/

20

14

1/2

007

-6/2

01

4

Seri

es in

lev

els

Levi

n, L

in a

nd

Chu

-3.4

52-4

.08

3-7

.121

-4.9

14

-2.6

60

-2.4

97

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36

-4.4

85

-9.2

82

-5.6

42-2

.14

4-3

.951

(0.0

09

)***

(0.0

06)*

**(0

.00

5)*

**(0

.01

0)**

(0.0

15)*

*(0

.01

9)*

*(0

.00

1)*

**(0

.03

4)*

*(0

.000

)***

(0.0

04)

***

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8)*

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Im, P

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13

(0.5

51

)(0

.438

)(0

.47

6)

(0.5

81)

(0.5

27

)(0

.35

9)(0

.74

3)

(0.5

22)

(0.0

41)*

*(0

.86

2)(0

.02

4)**

(0.3

9)**

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ng

0.36

31

.193

-0.8

701

.851

-0.9

27

-1.2

25

-0.3

44

0.9

42

-0.3

23

0.2

52

-0.2

88

-0.9

82

(0.2

47

)(0

.646

)(0

.019

)**

(0.9

76)

(0.0

09

)***

(0.0

03)

***

(0.0

07

)***

(0.0

84)*

(0.0

38)*

*(0

.02

0)*

*(0

.03

4)**

(0.0

07

)***

Fish

er A

DF

-1.4

28-2

.04

6-1

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-2.7

81

-2.5

47

-1.8

92

-1.6

76

-2.5

46

-2.0

47

-2.9

24-2

.49

2-1

.973

(0.6

69

)(0

.772

)(0

.72

7)

(0.8

31)

(0.8

08

)(0

.76

4)(0

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1)

(0.7

99)

(0.7

23

)(0

.85

4)(0

.802

)(0

.79

3)

Fish

er P

P-2

.038

-1.5

14

-1.1

13-1

.87

1-1

.09

0-1

.55

5-1

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5-1

.23

2-0

.88

1-1

.660

-1.0

59

-1.4

36

(0.6

57

)(0

.726

)(0

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9)

(0.8

31)

(0.9

66

)(0

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8)(0

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3)

(0.9

24)

(0.9

75

)(0

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6)(0

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)(0

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5)

Seri

es in

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st d

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n, L

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7.45

2-1

07.3

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481

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00

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.00

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Im, P

esa

ran

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n-6

0.3

84

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919

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316

-82

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)***

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.00

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Bre

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-30.

78

2-1

9.6

77-1

6.51

1-3

8.4

62-2

4.95

3-1

2.8

74

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00

)***

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00)*

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.00

0)**

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.00

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.00

0)**

*

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er A

DF

-10.

95

0-1

5.3

23-1

2.12

2-1

8.6

451

6.5

48-1

3.5

78

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00

)***

(0.0

00)*

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.00

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.00

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.00

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er P

P-3

9.5

70

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-40

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00

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00)*

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.00

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.00

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*

Indi

vid

ual e

ffec

ts +

indi

vid

ual t

rend

ln b

ee

fIn

divi

dua

l eff

ects

Tab

le 5

.1. R

esu

lts

of p

anel

un

it r

oot

test

s: B

eef

Not

e:

***,

**,

* d

enote

sig

nif

ican

ce a

t 1%

, 5%

an

d 10

%,

resp

ecti

vely

. p

-val

ues

are

rep

orte

d in

par

enth

eses

. P

rob

abil

itie

s fo

r F

ishe

r te

sts

are

com

put

ed u

sing

an

asym

ptoti

c C

hi-s

qu

are

dist

rib

uti

on.

All

oth

er t

ests

ass

ume

asym

ptot

ic n

orm

alit

y. S

elec

tio

n of

lag

s ba

sed

on

SIC

. N

ewey

-W

est

ban

dw

idth

sel

ecti

on u

sin

g B

artl

ett

kern

el. N

ull

hyp

othe

sis:

uni

t ro

ot.

60

EU1

5EU

15

EU2

5EU

15

EU2

5EU

27

EU1

5EU

15

EU2

5EU

15

EU2

5EU

27

2/1

99

5-6

/20

141/

20

05

-6/2

014

1/2

00

5-6

/20

141/

20

07

-6/2

01

41

/20

07-

6/2

01

41/

20

07

-6/2

01

42

/19

95-6

/20

14

1/2

00

5-6

/20

14

1/2

005

-6/2

01

41

/20

07-

6/2

014

1/2

007

-6/2

01

41

/20

07-

6/2

014

Se

rie

s in

leve

ls

Levi

n, L

in a

nd

Ch

u-4

.43

4-2

.895

-2.5

53

2.0

76

2.7

92

4.6

17

-6.2

71

-4.3

90

-1.7

23

2.4

72

4.76

67

.58

8

(0.0

04

)***

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05

)***

(0.0

18

)**

(0.2

57

)(0

.33

3)(0

.17

9)

(0.0

02)*

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.00

6)*

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.01

4)*

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.35

1)(0

.46

8)

(0.7

63

)

Im, P

esar

an a

nd

Shin

-0.6

22

-0.7

52-0

.43

4-0

.44

5-0

.27

9-0

.39

0-0

.27

7-0

.08

96

-0.8

12

-0.5

43

-0.6

14

-1.1

54

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56

)(0

.29

2)

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58

)(0

.45

7)

(0.5

72)

(0.4

69

)(0

.01

4)*

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.00

6)*

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.299

)(0

.04

1)*

*(0

.34

3)

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97

)

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itun

g-1

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0.3

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0.2

53

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20.

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0.4

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19

)**

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05

)***

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24

)(0

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63

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5)

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er A

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rie

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00

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1.1

26

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229

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5-6

2.8

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4

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00

)***

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00

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00

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00

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00

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00

)***

Fish

er A

DF

-24.

878

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7-1

2.33

2-1

7.7

11

-15

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00

)***

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Fish

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P-4

7.25

4-5

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(0.0

00

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Ind

ivid

ual e

ffec

tsIn

divi

dua

l eff

ects

+ in

divi

dua

l tre

nd

ln p

ork

Tab

le 5

.2. R

esu

lts

of p

anel

un

it r

oot

test

s: P

ork

Not

e:

***,

**,

* d

enot

e si

gni

fica

nce

at 1

%,

5%

and

10%

, re

spec

tive

ly.

p-v

alue

s ar

e re

por

ted

in

pare

nthe

ses.

Pro

bab

ilit

ies

for

Fis

her

test

s ar

e co

mp

uted

usi

ng

an a

sym

ptot

ic C

hi-s

qua

re d

istr

ibut

ion.

All

oth

er t

ests

ass

um

e as

ympt

otic

nor

mal

ity.

Sel

ecti

on o

f la

gs b

ased

on

SIC

. N

ewey

-Wes

t ba

ndw

idth

sel

ecti

on u

sin

g B

artl

ett

kern

el. N

ull

hyp

othe

sis:

uni

t ro

ot.

61

Unit root tests that assume cross-sectional independence can have low power if

estimated on data that have cross-sectional dependence. As a result, Pesaran's (2007)

CIPS test for unit roots is calculated. This is a second generation unit root test that

allows for cross-sectional dependence. Pesaran included cross-sectional averages of the

lagged levels as the common factor to filter out the cross-section dependence. The

statistic is constructed from the average of the cross-sectionally ADF (CADF) t-

statistics. The averaging of the group-specific results follows the procedure in the Im,

Pesaran and Shin (2003) test and brings out the CIPS t-statistic. Under the null of

nonstationarity the test statistic has a non-standard distribution. The critical values are

tabulated by the author for different combinations of N and T.

The panel unit root test results using CIPS test are reported in Table 5.3. These tests

were estimated with a constant term or with a constant term and trend. We choose to

include maximum 5 lags in each regression. We get qualitatively similar findings when

the number of lags in the regression is increased. Values of statistics for different

number of lags are reported for comparison purposes in order to assess the robustness of

the conclusion regarding non-stationarity of the data.

The Pesaran (2007) test is performed using the Stata -multipurt- command written by

Markus Eberhardt (2012).21

In this case the series were not stationary at the levels. So we made the regression

analysis with first differences of the variables. The results of the CIPS test indicate that

each series contains a unit root.

21 -multipurt- uses Scott Merryman's -xtfisher- and Piotr Lewandowski's -pescadf-.

62

EU1

5E

U1

5E

U2

5EU

15

EU

25

EU2

7E

U1

5EU

15

EU

25

EU

15

EU

25

EU

27

2/1

995

-6/2

014

1/2

005

-6/2

01

41

/20

05-

6/2

014

1/2

007

-6/2

014

1/2

00

7-6

/20

14

1/2

007

-6/2

014

2/1

995

-6/2

01

41

/20

05-6

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41

/200

5-6

/20

14

1/2

00

7-6

/20

141

/20

07-6

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41

/20

07

-6/2

01

4

Seri

es

in le

vels

LAG

1-1

.75

3-1

.55

6-1

.48

5-1

.52

1-1

.31

9-1

.21

0-2

.02

0-1

.81

3-1

.74

7-1

.77

8-1

.59

6-1

.50

2

LAG

2-1

.34

7-1

.59

8-1

.09

1-1

.31

9-1

.37

2-1

.06

3-1

.87

2-1

.83

3-1

.63

8-2

.55

6**

-1.6

17

-1.2

9

LAG

3-1

.42

4-1

.10

5-0

.89

1-1

.32

8-0

.95

3-0

.81

4-1

.95

1-1

.72

0-1

.42

0-1

.85

3-2

.18

1**

-1.3

62

LAG

4-1

.44

0-1

.21

9-1

.02

4-2

.04

5*

-1.4

25

-0.7

58

-2.7

52

**-1

.97

1-1

.94

4*

-1.5

78

-1.8

55

-1.4

96

LAG

5-1

.39

5-1

.34

6-0

.97

1-1

.75

7-1

.15

2-1

.54

3

Seri

es

in f

irst

dif

fere

nce

s

LAG

1-3

.25

6**

*-3

.07

1**

*-2

.82

7**

*-2

.79

0**

*-2

.54

8**

*-2

.50

1**

*

LAG

2-3

.21

*5**

-2.8

77

***

-2.7

16

***

-2.7

14

***

-2.6

84

***

-2.3

83*

**

LAG

3-3

.03

3**

*-2

.86

1**

*-2

.69

0**

*-2

.66

5**

*-2

.48

1**

*-2

.30

6**

LAG

4-4

.01

3**

*-3

.05

7**

*-2

.84

2**

*-2

.84

8**

*-2

.75

2**

*-2

.53

5**

*

LAG

5-3

.87

3**

*-2

.84

6**

*-2

.89

4**

*-2

.80

6**

*-2

.75

7**

*2

.66

4**

*

EU1

5E

U1

5E

U2

5EU

15

EU

25

EU2

7E

U1

5EU

15

EU

25

EU

15

EU

25

EU

27

2/1

995

-6/2

014

1/2

005

-6/2

01

41

/20

05-

6/2

014

1/2

007

-6/2

014

1/2

00

7-6

/20

14

1/2

007

-6/2

014

2/1

995

-6/2

01

41

/20

05-6

/201

41

/200

5-6

/20

14

1/2

00

7-6

/20

141

/20

07-6

/201

41

/20

07

-6/2

01

4

Seri

es

in le

vels

LAG

1-2

.45

5-2

.67

3-2

.69

0-2

.81

8-2

.88

1-2

.89

1-2

.87

4-2

.98

0**

-2.8

18

-3.0

42*

*-2

.95

5-3

.07

6*

LAG

2-2

.17

1-2

.25

3-2

.54

5-2

.73

6-2

.80

8-2

.78

2-2

.86

4-2

.88

1-2

.87

0-2

.93

8-2

.84

4-3

.21

5**

LAG

32

.06

7-2

.32

9-2

.38

1-2

.61

9-2

.79

2-2

.67

0-2

.80

62

.65

4-2

.48

2-2

.84

6-2

.69

1-2

.98

2*

LAG

4-2

.11

4-2

.47

2-2

.41

3-2

.55

7-2

.75

5-2

.70

6-2

.94

7**

-2.7

56

-2.7

15

-2.9

29

-2.7

56

-3.0

44

**

LAG

5-1

.98

1-2

.29

0-2

.33

3-2

.78

4-2

.69

2-2

.91

6-3

.05

5**

-2.8

45

-2.5

24

-2.9

92

*-2

.72

1-2

.89

0

Seri

es

in f

irst

dif

fere

nce

s

LAG

1-5

.01

7**

*-5

.63

7**

*-5

.09

6**

*-5

.45

4**

*-5

.67

3**

*-5

.56

5**

*

LAG

2-4

.86

8**

*-4

.93

7**

*-4

.67

3**

*-5

.02

4**

*-4

.88

1**

*-5

.29

0**

*

LAG

3-4

.60

8**

*-4

.87

1**

*-4

.92

3**

*-4

.89

5**

*-4

.86

6**

*-5

.01

4**

*

LAG

4-4

.87

5**

*-5

.33

6**

*-4

.94

4**

*-5

.23

1**

*-5

.69

0**

*-5

.28

0**

*

LAG

5-4

.71

7**

*-5

.47

6**

*-4

.83

3**

*-5

.08

4**

*-5

.32

2**

*-5

.41

7**

*

Ind

ivid

ual e

ffec

ts

Ind

ivid

ual e

ffec

tsIn

divi

dua

l eff

ects

+ in

divi

dual

tre

nd

Indi

vid

ual e

ffec

ts +

indi

vidu

al t

rend

ln b

ee

f

ln p

ork

Note

: *

**, *

*, *

den

ote

sig

nifi

can

ce a

t 1

%, 5

% a

nd 1

0%

, res

pec

tive

ly. N

ull

hyp

oth

esis

: un

it r

oot.

Tab

le 5

.3. R

esu

lts

of P

esa

ran

's C

IPS

tes

t

63

The first and second generation panel unit root tests may suffer from significant loss of

power if the data contains structural breaks. It is complicated to distinguish between unit

root and stationary processes with structural breaks. Today, the panel data unit root tests

which allow for structural breaks have received considerable attention among

econometricians. Therefore, we extend our analysis by performing Im et al. (2005)

panel LM test (ILT), that is an extension of the Lee and Strazicich’s (2004) minimum

LM test with one structural break, as well as the Im et al. (2010) test that allow for the

presence of heterogeneous structural breaks (ILT*) and cross-sectional dependence in

the data (ILTCA*). The objective of this analysis is to test unit root hypotheses in the

presence of structural change at the unknown time of the break.

In Table 5.4 we present the results of two versions of the panel LM test that is with and

without cross-sectional dependence, and perform the tests considering a heterogeneous

break only in level and both in level and trend. We employ the one-break version of the

panel LM tests of Im et al. (2005) (assuming cross-sectional independence) and Im et al.

(2010) (assuming cross-sectional dependence). The lag order selection are chosen using

the recursive t-statistic procedure with an upper bound of kmax=2. The null hypothesis of

the LM panel test is that all series contain unit roots, with the alternative that some of

the series in the panel are stationary.

The results show that in almost all cases, after taking into account the fact that the two

meat price series are subject to a structural break in mean and both in mean and the

slope of the series, the null hypothesis of a unit root is not rejected, indicating that these

series are not stationary with the presence of a structural break. The previous results

from unit root tests without breaks generally indicate that pork and beef price series

follow unit root process. Allowing for a structural break does not change the results

from non-stationary to stationary. However, it should be noted that if we allow more

than one structural break point, the results could be altered.

To make the analysis robust, the results of panel data unit root tests are compared with

those obtained with individual unit root tests. Appendix 1 includes LM unit root test

results for each individual country assuming one structural breakpoint. We do not

consider structural break-dates as being exogenously determined, but we test

endogenously for them, i.e. assuming the break-date to be unknown. According to the

results in 55 of the 67 pork series and in 49 of the 67 beef series, the unit root null

cannot be rejected at the 10% significance level. According to the break locations, there

seems to be many important events that have caused significant breaks in the time paths

of price series in the EU member states.

64

EU15

EU15

EU25

EU15

EU25

EU27

EU15

EU15

EU25

EU15

EU25

EU27

2/19

95-6

/201

41/

2005

-6/2

014

1/20

05-6

/201

41/

2007

-6/2

014

1/20

07-6

/201

41/

2007

-6/2

014

2/19

95-6

/201

41/

2005

-6/2

014

1/20

05-6

/201

41/

2007

-6/2

014

1/20

07-6

/201

41/

2007

-6/2

014

C-S

Ind

ep

en

de

nce

ILT

-0.6

21-0

.375

-0.0

98-0

.846

-1.3

10-1

.210

ILT*

1.5

742.

885

0.75

32.

267

0.55

51.

068

1.07

51.

852

0.71

22.

086

0.47

10.

656

C-S

De

pe

nd

en

ce

ILT C

A*

5.2

174.

267

2.98

26.

334

1.58

73.

014

-3.5

76**

-3.1

84**

-2.0

95-2

.894

-1.6

48-1

.286

EU15

EU15

EU25

EU15

EU25

EU27

EU15

EU15

EU25

EU15

EU25

EU27

2/19

95-6

/201

41/

2005

-6/2

014

1/20

05-6

/201

41/

2007

-6/2

014

1/20

07-6

/201

41/

2007

-6/2

014

2/19

95-6

/201

41/

2005

-6/2

014

1/20

05-6

/201

41/

2007

-6/2

014

1/20

07-6

/201

41/

2007

-6/2

014

C-S

Ind

ep

en

de

nce

ILT

-1.5

23-1

.222

-1.8

25-1

.085

-2.3

68-1

.454

ILT*

5.8

524.

561

6.11

34.

219

2.69

63.

224

2.00

52.

854

3.04

51.

328

1.78

91.

151

C-S

Ind

ep

en

de

nce

ILT C

A*

9.6

5210

.854

7.56

28.

881

5.75

46.

229

-2.3

43-1

.975

-2.8

58-1

.567

-2.6

41-2

.753

Leve

lLe

vel +

tre

nd

Leve

lLe

vel +

tre

nd

ln b

ee

f

ln p

ork

Not

e:

***,

**,

* d

enot

e si

gni

fica

nce

at 1

%,

5% a

nd

10%

, re

spec

tive

ly. N

ull

hyp

oth

esis

: un

it r

oot.

Tab

le 5

.4. R

esu

lts

of p

anel

LM

tes

ts w

ith

on

e st

ruct

ura

l br

eak

65

We employed a series of panel unit root tests that assume cross sectional independence

(the so called first generation panel data unit root tests) and cross-sectional dependence

(second generation panel data unit root tests). These tests do not allow for structural

breaks. For this reason we completed our analysis by employing the Lagrange

Multiplier (LM) panel unit root test with a break. In summary, the results of panel unit

root test we have employed indicate quite unanimously that both beef and pork series

contain a unit root and are integrated of order one, I(1). Next, we can carry out the

second part of the empirical analysis (panel cointegration tests) and check for the

existence of a long-run relationship between the price variables.

5.2 Cointegration test results

We are interested in the long-run equilibrium relationship between the beef and pork

prices in the EU. However, it cannot be consistently estimated if single series have a

unit root, unless they are cointegrated in the long run. Thus, the next step of our analysis

is to perform panel cointegration tests.

In panel settings, there are several ways of testing the null hypothesis of no cointegra-

tion. Namely, these test are grouped in two large families: the residual-based ones

(Pedroni, 1999, 2004; Kao, 1999), constructed on the basis of the Engle and Granger’s

(1987) two step test and likelihood-based ones (Maddala and Wu, 1999) which

represent the generalization of the widely-used Johansen (1991, 1996) test for vector

autoregressive models to panel data. It proceeds by applying the Johansen test to each

cross-section unit individually and then combining the marginal significance values for

each of these to arrive at a single p-value. Also, we use panel cointegration tests

developed by Westerlund (2007) and Westerlund and Edgerton (2007) that are based on

the structural dynamic, as opposed to the residual dynamic. The main idea is to test the

null hypothesis of no cointegration by inferring whether the error-correction term in the

conditional panel error-correction model is equal to zero.

In Table 5.5, we consider Pedroni test with deterministic intercept and trend.

Deterministic time trend has been included to account for cross sectional dependence.

Among the seven Pedroni's panel cointegration tests, four are based on the within

dimension (panel cointegration statistics) and the three others on the between dimension

(group mean panel cointegration statistics). All tests are based on the null hypothesis of

no cointegration for all countries. Under the alternative hypothesis, for the panel

statistics, there is cointegration for all countries i. However, the group statistics allow

for heterogeneity across countries under the alternative hypothesis.

66

The estimations are performed in both directions with the dependent and independent

variables interchanged to find evidence for the existence of a bi-directional relationship

between the pork and beef prices. The majority of Pedroni’s test statistics for

heterogeneous panels indicate the possibility of a bi-directional cointegrating (or long-

run equilibrium) relationship between pork and beef prices. Overall, we can claim that

only one among the seven statistics used by the Pedroni test (ie. Panel ν-statistics) does

not reject the null hypothesis of no cointegration. The cointegrating relationship seems

to be stronger when we use the beef price series as a dependent variable.

EU1

5EU

15

EU2

5EU

15

EU2

5EU

27

EU1

5EU

15

EU2

5EU

15

EU2

5EU

27

2/1

99

5-6

/20

14

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00

5-6

/20

14

1/2

005

-6/2

014

1/2

007

-6/2

014

1/2

007

-6/2

014

1/2

007

-6/2

014

2/1

995

-6/2

014

1/2

005

-6/2

014

1/2

00

5-6

/20

14

1/2

00

7-6

/20

14

1/2

00

7-6

/20

14

1/2

00

7-6

/20

14

Pa

nel C

oin

teg

rati

on S

tati

stic

s (W

ith

in-D

imen

sio

n)

Pan

el ν

-sta

tist

ics

2.2

622

.698

1.6

383

.747

2.5

452

.290

1.9

54

2.4

78

1.0

25

0.8

85

1.4

251

.653

(0.0

31)*

*(0

.011

)**

(0.0

93)*

(0.0

00)*

**(0

.01

6)*

*(0

.02

9)**

(0.0

57)*

(0.0

12

)**

(0.1

16

)(0

.30

5)

(0.0

88

)*(0

.07

1)*

Pan

el P

P ρ

-sta

tist

ics

-5.8

23

-3.8

75

-3.1

12

-5.7

06

-2.4

35-2

.809

-2.4

35-2

.603

-2.0

58-2

.914

-2.3

11

-2.2

21

(0.0

00)*

**(0

.000

)***

(0.0

13)*

*(0

.000

)***

(0.0

21

)**

(0.0

15)

**(0

.01

2)*

*(0

.00

8)*

**(0

.05

5)*

(0.0

05

)***

(0.0

10)*

*(0

.021

)**

Pan

el P

P t

-sta

tist

ics

-5.0

64

-5.8

88

-4.4

46

-4.7

73

-3.2

83-2

.849

-2.8

38-2

.031

-3.0

47-1

.878

-3.0

02

-2.6

22

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.000

)***

(0.0

02)*

**(0

.00

7)*

**(0

.01

5)*

*(0

.08

3)*

(0.0

23)*

*(0

.00

4)*

**(0

.020

)**

(0.0

17)*

*

Pan

el A

DF

t-st

atis

tics

-10.

577

-12.

544

-14.

886

-11.

42

9-9

.005

-7.0

29-6

.132

-7.1

17-5

.027

-8.1

65-5

.74

9-3

.81

9

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.00

0)*

**(0

.00

7)*

**(0

.00

0)*

**(0

.07

1)*

(0.0

00

)***

(0.0

54

)*(0

.12

5)

Gro

up

Mea

n P

anel

Co

inte

gra

tio

n St

ati

stic

s (B

etw

ee

n-D

imen

sion

)

Gro

up P

P ρ

-sta

tist

ics

-3.9

59

-5.7

11

-2.2

62

-3.3

44

-2.7

48-1

.855

-2.8

45-3

.559

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07-3

.985

-2.3

60

-1.7

62

(0.0

00)*

**(0

.000

)***

(0.0

25)*

*(0

.002

)***

(0.0

11

)**

(0.0

71)*

(0.0

00

)***

(0.0

00

)***

(0.0

28)*

*(0

.00

0)*

**(0

.025

)**

(0.1

31

)

Gro

up P

P t

-sta

tist

ics

-4.5

64

-5.6

33

-7.9

47

-6.3

85

-5.7

06-6

.91

-2.8

11-3

.384

-3.8

75-6

.135

-4.5

64

-5.8

28

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.00

0)*

**(0

.00

5)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.000

)***

(0.0

00)*

**

Gro

up A

DF

t-st

atis

tics

-12.

078

-15.

125

-14.

281

-13.

11

2-9

.179

-10

.73

3-7

.316

-8.3

04-6

.926

-9.0

01-8

.07

8-6

.40

4

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.000

)***

(0.0

00)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.00

0)*

**(0

.000

)***

(0.0

09)*

**

Num

ber

of

test

sta

tist

ics

that

rej

ect

H0

at 5

%

leve

l

7/7

7/7

6/7

7/7

7/7

6/7

6/7

6/7

4/7

6/7

5/7

4/7

ln P

ork

Dep

. var

. of

coin

t. r

eg.

Dep

. var

. o

f co

int.

reg

.

ln B

eef

Tab

le 5

.5.

Res

ult

s of

Ped

ron

i re

sidu

al

coin

tegr

atio

n t

ests

Not

e:

***,

**,

* d

enot

e si

gni

fica

nce

at 1

%,

5%

and

10

%,

resp

ecti

vely

. p

-val

ues

are

rep

orte

d i

n pa

rent

hese

s. S

elec

tio

n of

lag

s ba

sed

on

SIC

. New

ey-W

est

ban

dw

idth

sel

ecti

on u

sin

g B

artl

ett

kern

el. N

ull

hypo

thes

is:

no c

oint

egra

tio

n.

67

The Kao (1999) test follows the same basic approach as the Pedroni’s tests. Totally, Kao

proposed four DF-type panel cointegration tests based on the OLS residuals from the

homogeneous panel regression. Kao also uses ADF-type panel cointegration test for

cointegration in heterogeneous panel. In this test version, the parameters are allowed to

differ across the cross-sections. In our study, we only report Kao’s ADF- type test.

In the null hypothesis, the residuals are nonstationary (there is no cointegration) and in

the alternative hypothesis, the residuals are stationary (there is a cointegrating

relationship among the variables). Table 5.6 reports the results of Kao’s residual panel

cointegration tests, which reject the null of no cointegration at the 5% significance level

in most of the specifications. Findings of Kao’s residual cointegration test corroborates

the findings of Pedroni’s residual cointegration test in Table 5.5.

Both the Pedroni and the Kao tests use the Schwartz Information Criterion (SIC) to

automatically select the appropriate lag length. Further, spectral estimation is

undertaken by the Bartlett kernel with the bandwidth selected by the Newey-West

algorithm. Deterministic time trends are included in all specifications.

68

EU1

5EU

15

EU2

5EU

15

EU2

5EU

27

EU1

5EU

15

EU2

5EU

15

EU2

5EU

27

2/1

995

-6/2

01

41

/20

05

-6/2

014

1/2

00

5-6

/20

14

1/2

007

-6/2

01

41

/20

07

-6/2

014

1/2

007

-6/2

014

2/1

995

-6/2

014

1/2

00

5-6

/20

141

/20

05-6

/20

14

1/2

00

7-6

/20

141

/20

07

-6/2

01

41

/20

07-6

/20

14

AD

F-st

atis

tics

-1.6

42

-2.5

52

-0.9

50

-3.2

51

-1.2

38

-1.4

44

-1.3

22

-3.2

47

-0.7

89

-2.6

61

-1.5

21

-1.7

28

(0.0

02)

***

(0.0

00)

***

(0.0

41

)**

(0.0

00)

***

(0.0

71)

*(0

.02

5)*

*(0

.00

5)**

*(0

.00

0)*

**(0

.06

6)*

(0.0

00)

***

(0.0

35

)**

(0.0

02)

***

Dep

. var

. of

coin

t. r

eg.

Dep

. var

. o

f co

int.

reg

.

ln B

ee

fln

Po

rk

Not

e:

***

, **

, *

den

ote

sig

nif

ican

ce a

t 1

%,

5%

and

10%

, re

spec

tive

ly.

p-v

alue

s ar

e re

por

ted

in

pare

nthe

ses.

Sel

ecti

on o

f la

gs b

ased

on

SIC

. N

ewey

-Wes

t

ban

dw

idth

sel

ecti

on

usin

g B

artl

ett

kern

el. N

ull

hyp

oth

esis

: n

o co

inte

grat

ion.

Tab

le 5

.6. R

esu

lts

of K

ao r

esid

ual

coi

nte

grat

ion

tes

t

69

Since we might have cross-sectional dependence in our series, cross-sectional

dependence in cointegration vectors is likely. Therefore, we perform the Westerlund

(2007) cointegration test with bootstrap under the assumption of cross-sectional

dependence.

Westerlund (2007) developed four new panel cointegration tests that are based on

structural dynamics and do not impose any common-factor restriction. The idea is to test

whether the error-correction term in the panel error-correction model is equal to zero.

The four kinds of tests can be divided into two groups by the difference of the

alternative hypotheses. The two tests called group-mean tests are designed to test the

alternative hypothesis that at least one unit is cointegrated, while the panel tests are

designed to test the alternative hypothesis that the panel is cointegrated as a whole.

These two tests can be used in both cases of cross-sectional dependency and

independence cases. These tests allow also heterogeneity among the units forming the

panel.

We adopt the Westerlund (2007) panel cointegration tests selecting the lead and lag

orders based on the minimum AIC (Akaike’s Information Criterion) and the Bartlett

kernel window width. We perform cointegration tests with both a constant and a trend.

To take consideration of the cross-sectional dependence we introduce bootstrap into the

test to get the robust critical values for the test statistics. The number of replication for

the bootstrap is 2000.22

The Westerlund’s test takes no cointegration as the null hypothesis. Table 5.7 below

report the panel cointegration results. All the group mean panel cointegratin statistics

and most of the panel cointegration statistics reject the null of no cointegration at the

5% level of significance. Overall, the null hypothesis of no-cointegration is rejected in

both asymptotic standard distribution and in bootstrap method. The results suggest that

cointegration relationship exists between the series and they are expected to move

together in the long run.

22 We then used Stata command -xtwest- to test for cointegration.

70

EU1

5EU

15

EU2

5E

U1

5E

U2

5EU

27

EU1

5EU

15

EU2

5EU

15

EU

25

EU

27

2/1

99

5-6

/20

14

1/2

00

5-6

/20

14

1/2

00

5-6

/20

14

1/2

00

7-6

/20

14

1/2

007

-6/2

01

41

/20

07-6

/20

14

2/1

995

-6/2

014

1/2

00

5-6

/20

14

1/2

00

5-6

/20

14

1/2

007

-6/2

01

41

/20

07-6

/20

14

1/2

007

-6/2

01

4

Gro

up M

ean

Pan

el C

oin

teg

rati

on

Sta

tist

ics

Gτ

-9.2

11

-12

.755

-7.5

42-8

.221

-13

.63

5-1

1.7

9-1

0.4

61-9

.35

7-7

.15

9-8

.446

-10

.95

4-1

2.3

49

asym

pto

tic

p-va

lue

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

boo

tsta

p p

-va

lue

(0.0

02)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

Gα

-8.9

28

-10

.212

-8.3

17-1

1.4

26

-15

.66

2-9

.34

3-9

.52

3-9

.87

6-1

0.2

25-1

2.3

13

-12

.84

5-1

0.6

12

asym

pto

tic

p-va

lue

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

boo

tsta

p p

-va

lue

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

Pane

l Co

inte

grat

ion

Stat

isti

cs

P τ-5

.71

5-8

.14

5-4

.021

-3.6

46-7

.722

-7.1

48

-5.2

13

-7.8

58

-4.1

18

-3.4

29-7

.058

-6.9

24

asym

pto

tic

p-va

lue

(0.0

01)*

**(0

.000

)***

(0.0

02

)***

(0.0

04

)***

(0.0

00

)***

(0.0

02

)***

(0.0

01

)***

(0.0

00)*

**(0

.002

)***

(0.0

05

)***

(0.0

00

)***

(0.0

03

)***

boo

tsta

p p

-va

lue

(0.0

07)*

**(0

.002

)***

(0.0

05

)***

(0.0

10

)**

(0.0

03

)***

(0.0

03

)***

(0.0

08

)***

(0.0

02)*

**(0

.006

)***

(0.0

10

)**

(0.0

03

)***

(0.0

04

)***

P α-5

.22

4-8

.45

7-3

.228

-6.1

13-3

.832

-4.5

57

-4.8

88

-8.3

71

-3.3

47

-6.0

89-3

.665

-4.4

26

asym

pto

tic

p-va

lue

(0.0

00)*

**(0

.000

)***

(0.0

00

)**

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)**

(0.0

00)*

**(0

.000

)***

(0.0

00

)***

(0.0

00

)***

(0.0

00

)***

boo

tsta

p p

-va

lue

(0.0

45

)**

(0.0

16

)**

(0.2

18

)(0

.03

6)*

*(0

.18

4)

(0.0

56

)*(0

.05

0)*

(0.0

20

)**

(0.1

92

)(0

.03

8)*

*(0

.18

5)

(0.0

58

)*

Dep

. va

r. o

f co

int.

reg

. ln

Po

rkD

ep.

var.

of

coin

t. r

eg.

ln B

ee

f

Tab

le 5

.7.

Res

ult

s of

Wes

terl

un

d’s

seco

nd-

gen

erat

ion

pan

el c

oin

tegr

atio

n

test

s

Not

e:

***

, **,

* d

enot

e si

gnif

ican

ce a

t 1

%,

5%

and

10%

, re

spec

tive

ly.

Tes

t re

gres

sion

is

fitt

ed w

ith

a co

nsta

nt a

nd o

ne l

ag a

nd

lead

wit

h th

e ke

rnel

ban

dw

idth

bei

ng

set

acco

rdin

g t

o t

he r

ule 4

(T/1

00)2/

9 . p

-val

ues

are

rep

orte

d in

par

enth

eses

. T

he p

-val

ues

are

for

a o

ne-s

ided

tes

t ba

sed

on 2

000

bo

ots

trap

rep

lica

tion

s. N

ull

hypo

thes

is:

no c

oin

tegr

atio

n.

71

In order to assess the robustness of our findings, we also implemented the panel

cointegration test proposed by Westerlund and Edgerton (2007). Unlike the other panel

data cointegration tests, here the null hypothesis is now cointegration. This test relies on

the popular Lagrange multiplier test of McCoskey and Kao (1998), and permits

correlation to be accommodated both within and between the individual cross-sectional

units. The test is also robust to unknown heterogeneous breaks in the intercept and/or

the slope of the cointegrating regression. In addition, the bootstrap suggested by

Westerlund and Edgerton (2007) is based on the sieve-sampling scheme23, and has the

appealing advantage of significantly reducing the distortions of the asymptotic test.

The results reported in Table 5.8 for a model including a constant and a trend clearly

indicate the cointegrating relationship between pork and beef prices in the EU since the

null hypothesis of cointegration is always accepted. This result is not modified if one

refers to the bootstrap critical values compared to asymptotic ones. Results also confirm

that the cointegrating relationship is robust to potential level and regime shifts.

The last panel cointegration test applied is the Johansen-Fisher type panel cointegration

test developed by Maddala and Wu (1999). They use Fisher’s result to propose an

alternative approach to test for cointegration in panel data by combining tests from

individual cross-sections to obtain a test statistic for the full panel. The Johansen-Fisher

panel cointegration test is a panel version of the individual Johansen cointegration test.

The Johansen-Fisher panel cointegration test is based on the aggregates of the p-values

of the individual Johansen maximum eigenvalues and trace statistic.

The results from Johansen-Fisher panel cointegration test are presented in Table 5.9. We

used the Akaike Information Criterion (AIC) and the Schwarz Information Criterion

(SIC) to determine the optimal lag length. We perform the cointegration test with both a

constant without trend and a constant with trend.

23 Sampling method proposed by Rietveld (1978), a simple and practical strategy for selecting line items with PPS (probability to proportional to line item size).

72

EU

15

EU1

5EU

25

EU

15

EU2

5E

U2

7EU

15

EU

15

EU2

5E

U1

5E

U2

5EU

27

2/1

99

5-6

/20

14

1/2

00

5-6

/20

14

1/2

00

5-6

/20

141

/20

07-

6/2

01

41

/20

07

-6/2

01

41

/20

07-

6/2

01

42

/19

95

-6/2

01

41

/20

05-

6/2

01

41

/20

05

-6/2

01

41

/20

07

-6/2

014

1/2

00

7-6

/20

14

1/2

00

7-6

/20

14

LM-s

tat

1.0

54

1.3

47

1.5

48

2.0

12

1.7

58

1.9

64

2.0

42

2.1

87

1.8

38

2.5

73

2.2

25

2.3

49

asym

pto

tic

p-va

lue

(0.5

15)

(0.3

86

)(0

.21

1)

(0.2

51

)(0

.18

7)

(0.1

98)

(0.3

35

)(0

.30

9)(0

.22

7)

(0.2

64

)(0

.34

9)

(0.3

76

)

boo

tsta

p p

-va

lue

(0.3

19)

(0.2

04

)(0

.25

1)

(0.1

28

)(0

.15

7)

(0.1

41)

(0.1

02

)(0

.08

2)(0

.12

6)

(0.0

66

)(0

.11

6)

(0.0

84

)

Dep

. va

r. o

f co

int.

reg

. ln

Be

ef

Dep

. var

. o

f co

int.

reg

. ln

Po

rk

Tab

le 5

.8.

Res

ult

s of

sec

ond-

gen

erat

ion

pa

nel

coi

nte

grat

ion

tes

t pr

opos

ed b

y W

este

rlu

nd

and

Edg

erto

n

Note

: *

**,

**,

* d

enot

e si

gnif

ican

ce a

t 1%

, 5%

and

10%

, re

spec

tive

ly.

Tes

t re

gres

sion

is

fitt

ed w

ith

a co

nsta

nt a

nd o

ne l

ag a

nd l

ead

wit

h th

e ke

rnel

ba

ndw

idth

bei

ng s

et a

ccord

ing

to

th

e ru

le

4(T

/100

)2/9 .

p-v

alue

s ar

e re

por

ted

in p

aren

thes

es.

The

p-v

alue

s ar

e fo

r a

one-

side

d te

st b

ased

on

2000

bo

ots

trap

rep

lica

tions

. The

Wes

terl

und

an

d E

dge

rton

(2

007)

tes

ts a

re p

erfo

rmed

usi

ng t

he S

tata

“xt

wes

t” c

om

man

d. N

ull

hyp

othe

sis:

co

inte

grat

ion.

Tab

le 5

.9. R

esu

lts

from

th

e Jo

ha

nse

n-F

ish

er p

anel

coi

nte

grat

ion

tes

t

Not

e:

***,

**,

* de

note

sig

nifi

can

ce a

t 1%

, 5

% a

nd 1

0%,

resp

ecti

vely

. p

-val

ues

are

rep

orte

d i

n pa

rent

hese

s. S

elec

tion

of

lags

bas

ed o

n A

IC a

nd

SIC

. N

ull

hyp

oth

esis

: no

coi

nteg

rati

on.

73

Based on the maximal eigenvalue statistics, we reject the null hypothesis of no

cointegrating relationship. Results from the trace statistics also support the finding of

maximal eigenvalue statistics where there is rejection towards null hypothesis of r=0

and r≤1, respectively. In a nutshell, based on the statistical results of the Johansen-

Fisher panel cointegration test, there is sufficient evidence to conclude the existence of

the long run relationship between the pork and beef prices.

In summary, the results of panel cointegration tests are generally highly significant

which gives strong evidence that the variables have a long run relationship.

5.3 Estimation results

The cointegration tests are only able to indicate whether or not the variables are

cointegrated and if a long-run relationship exists between them. Since they do not

indicate the direction of causality, the long-run equilibrium coefficients should be

estimated by using appropriate estimators.

When panels of data are available, there exist a number of alternative estimation

methods that vary on the extent to which they account for parameter heterogeneity. The

dynamics of traditional estimators is simply pooled and treated as homogeneous. Only

the intercepts are allowed to differ cross countries. Early and prominent examples

include fixed effects (FE), random effects (RE), and generalized methods of moments

(GMM). These methods are typically focused on solving the problem of fixed effect

heterogeneity in the case of large N and small T panels; whereas they are not designed

to correct for the endogeneity induced by the latent heterogeneity. Pesaran and Smith

(1995) show that the traditional procedures for the estimation of pooled models can

produce inconsistent and potentially misleading estimates of the lagged dependent

variable’s parameter in dynamic panel data models if latent heterogeneity is present.

In this study, we utilize panel-based vector error correction models (VECM) including

MG (Mean Group) and PMG (Pooled Mean Group) estimation methods. The MG

estimator allows for complete (short-run and long-run) parameter heterogeneity across

panel cross-sections. The PMG estimator is an intermediate case between the averaging

and pooling methods of estimations.24

24 OLS estimators are super-consistent in the case of co-integrated variables, but they are based on strong homogeneity assumptions among countries by imposing single slope coefficient in pooled estimation, which is inappropriate for this study regarding potential country heterogeneity. This is the reason for using MG and PMG estimators instead of traditional panel techniques.

74

The first technique, MG, introduced by Pesaran and Smith, (1995) calls for estimating

separate regressions for each country and calculating the coefficients as unweighted

means of the estimated coefficients for the individual countries. It allows for all

coefficients to vary and be heterogeneous in the long-run and short-run. Pesaran and

Smith showed that the MG method produces consistent estimates of the average of the

parameters when the time-series dimension of the data is sufficiently large. However,

for small N the average estimators (MG) in this approach are quite sensitive to outliers

and small model permutations (Favara, 2003).

The MG estimator does not take into account that some economic conditions tend to be

common across countries in the long run. The PMG estimator does because it combines

both pooling and averaging. The long run coefficients are constrained to be the same

across sections, while the intercepts, short run coefficients, and error variances are

allowed to differ. The PMG estimator is believed to offer the best compromise between

consistency and efficiency, because one would expect the long-term growth path to be

driven by a similar process across EU member states while the short-term dynamics

around the long-term equilibrium path differ because of idiosyncratic member state

features and shocks to fundamentals. The PMG estimator is appropriate when data have

complex country-specific short-term dynamics which cannot be captured imposing the

same lag structure on all countries. This estimator combines the properties of efficiency

of the pooled dynamic estimators while avoiding the inconsistency problem deriving

from slope heterogeneity. Moreover, since the PMG estimator does not impose any

restriction on short–term coefficients, it provides important information on country

specific values of the speed of convergence towards the long-run relationship linking

pork and beef price series.

The variables are I(1) and cointegrated, which means that the error term is I(0) process

for all i. A principal feature of cointegrated variables is their responsiveness to any

deviation from long-run equilibrium. This feature implies an error correction model in

which the short-run dynamics of the variables in the system are influenced by the

deviation from equilibrium. Since the model is nonlinear in the parameters, Pesaran,

Shin, and Smith (1999) develop a maximum likelihood method to estimate the

parameters.

The error-correction model to be estimated is given by the following equations: �� =

��,�� − �� + ∑ �� ∆��,�� + ∑ ��

�� ∆��,�� +

�� + �� (5.1)

75

�� = �′��,�� − �′�� + ∑ �′�� ∆��,�� +

∑ �′�� ∆��,�� + �′� + �′�� (5.2)

where the number of groups i=1,2,…,N and the number of periods t=1,2,…,T. δij are the

coefficient vectors, �ij are scalars and μi is the group specific effect. θi is the vector

which contains the long-run relationships between the variables and the parameter φi is

the error-correcting speed of adjustment term. The long run coefficients are estimated

using the Maximum Likelihood (ML) estimation

The long-run relationship imposes under the null hypothesis the condition: φi = 0 for all

i. This hypothesis means that there is no long-run stable relationship between the

independent variable and the dependent variable in the model. The decision rule says

that when the error correction term (ECT) is negative and significant, the null

hypothesis of no causality would be rejected.

ECT is a measure of the extent by which the observed values in time t-1 deviate from

the long-run equilibrium relationship. Since the variables are cointegrated, any such

deviation at time t-1 should induce changes in the values of the variables in the next

time point, in an attempt to force the variables back to the long-run equilibrium

relationship.

In practice, the MG and PMG procedure involves first estimating autoregressive

distributed lag (ARDL) models separately for each country i. In a series of papers,

Pesaran and Smith (1995), Pesaran (1997), and Pesaran and Shin (1999) show that one

can use the ARDL approach to produce consistent and efficient estimates of the

parameters in a long-run relationship between both integrated and stationary variables,

and to conduct inference on these parameters using standard tests. The main require-

ments for the validity of this methodology are that, first, there exists a long-run

relationship among the variables of interest and, second, the dynamic specification of

the model is sufficiently augmented so that the regressors become weakly exogenous

and the resulting residual is serially uncorrelated25.

Tables 5.10 and 5.11 report MG and PMG estimates of the ECM. We have already

revealed that there is bi-directional relationship between pork and beef prices in the EU.

Thus, the dependent variable in the estimation results of Table 5.10 is lnbeef and

25 Augmenting the model with lags addresses the potential endogeneity of remittances. In this respect, Pesaran (1997) and Pesaran and Shin (1999) show that for inference on the long-run parameters, sufficient augmentation of the order of the autoregressive distributive lag model can simultaneously correct for the problem of residual serial correlation and endogenous regressors.

76

correspondingly the dependent variable is lnpork in Table 5.11 results. Lags are chosen

on the basis of AIC and are allowed to vary across countries. We report results both with

and without trend. A constant country-specific term is included. Natural logarithms are

used in order to obtain directly the elasticities.

We analyze the stability of our results over time by re-estimating the models for EU15

countries in two sub periods (1/2005-6/2014 and 1/2007-6/2014) and for EU25

countries in one subperiod (1/2007-6/2014). This is also a check for cross-sectional

stability: we can compare if new member state distort the estimation results.

77

tren

dn

o t

ren

dtr

end

no

tre

nd

tre

nd

no

tre

nd

tre

nd

no

tre

nd

tren

dn

o t

ren

dtr

end

no

tre

nd

tren

dn

o t

ren

dtr

end

no

tre

nd

tren

dn

o t

ren

dtr

end

no

tre

nd

tren

dn

o t

ren

dtr

end

no

tre

nd

Lon

g R

un

ln p

ork

0.25

10.

253

0.28

20.

288

0.21

30.

217

0.42

30.

426

0.4

090.

413

0.3

920.

394

0.1

930.

197

0.2

150.

219

0.14

70.

155

0.31

50.

331

0.28

10.

308

0.30

10.

310

(10.

385

)***

(12.

127)

***

(16.

291

)***

(18.

754)

***

(6.3

72)*

**(6

.479

)***

(29.

490)

***

(30.

572

)***

(27.

477)

***

(28.

582

)***

(25.

115)

***

(25.

626

)***

(16.

476)

***

(17.

554

)***

(19.

889)

***

(21.

216

)***

(10.

124

)***

(12.

397)

***

(33.

262

)***

(36.

661)

***

(32.

910

)***

(35.

090)

***

(29.

431

)***

(33.

995)

***

tim

e tr

en

d-0

.001

0.00

2-0

.002

0.00

1-0

.002

-0.0

01-0

.002

-0.0

01-0

.001

-0.0

02-0

.001

-0.0

03

(-2.

714)

**(2

.217

)**

(-2.

456)

**(1

.694

)(-

1.8

80)

(-1.

755)

(-2.

113)

*(-

1.68

6)(-

1.95

8)*

(-1.

224)

(-1

.517

)(-

2.43

7)**

Sho

rt R

un

ECT

(ad

j. s

pee

d)

-0.0

40-0

.042

-0.0

61-0

.065

-0.0

55-0

.057

-0.0

98-0

.099

-0.0

85-0

.085

-0.1

01-0

.105

-0.0

35-0

.038

-0.0

57-0

.06

-0.0

47-0

.054

-0.0

86-0

.093

-0.0

71-0

.073

-0.0

82-0

.088

(-1

1.11

8)**

*(-1

3.5

46)*

**(-

15.6

43)*

**(-

17.1

19)*

**(-

14.5

54)*

**(-

15.7

25)*

**(-

20.4

39)*

**(-

21.4

77)*

**(-

16.9

86)*

**(-

18.

442)

***(

-22.

971

)***

(-24

.811

)***

(-8

.530

)***

(-9.

158)

***(

-12.

227

)***

(-12

.972

)***

(-13

.114

)***

(-13

.785

)***

(-17

.218

)***

(-19

.076

)***

(-1

4.96

2)**

*(-1

5.9

21)*

**(-

22.1

24)*

**(-

21.8

50)*

**

Δ ln

po

rk-0

.003

-0.0

03-0

.010

-0.0

110.

006

0.0

070.

050

0.05

10.

061

0.06

20.

036

0.03

8-0

.002

-0.0

03-0

.017

-0.0

19-0

.005

-0.0

05-0

.037

-0.0

47-0

.072

-0.0

74-0

.055

-0.0

68

(-1.

828)

*(-

1.8

59)*

(-1.

231

)(-

1.26

1)(2

.290

)**

(2.4

29)*

*(3

.154

)**

(3.1

57)*

*(3

.793

)***

(3.8

46)*

**(2

.208

)**

(2.2

11)*

*(-

1.48

5)(-

1.5

56)

(-1.

237)

(-1.

281

)(-

2.02

8)*

(-2.

123

)*(-

2.17

3)*

(-2

.445

)**

(-3.

906

)***

(-3.

949)

***

(-2.

682)

**(-

3.0

19)*

**

Δ ln

po

rkt-

1-0

.001

-0.0

01-0

.002

-0.0

02-0

.001

-0.0

010.

015

0.01

70.

023

0.02

30.

009

0.00

9-0

.001

-0.0

01-0

.001

-0.0

020.

004

0.00

50.

008

0.0

100.

007

0.0

070.

003

0.0

05

(-0

.944

)(-

0.95

4)(-

1.1

07)

(-1.

136)

(-0.

753

)(-

0.76

1)(2

.685

)**

(2.6

82)*

*(2

.508

)**

(2.5

18)*

*(1

.957

)*(1

.974

)*(-

0.66

2)(-

0.7

33)

(-0.

940)

(-1.

019

)(1

.750

)(1

.828

)(1

.938

)*(2

.181

)*(-

1.8

74)

(1.9

55)*

(1.4

78)

(1.6

10)

Δ ln

po

rkt-

20.

001

0.0

010.

002

0.0

030.

001

0.0

010.

001

0.00

10.

002

0.00

30.

001

0.00

1-0

.001

-0.0

020.

002

0.00

32-0

.001

-0.0

01-0

.001

-0.0

01-0

.001

-0.0

01-0

.001

-0.0

01

(0.4

09)

(0.4

01)

(0.8

07)

(0.8

73)

(0.5

45)

(0.5

55)

(1.2

24)

(1.2

39)

(1.5

87)

(1.6

26)

(0.3

71)

(0.3

90)

(-0.

274)

(0.3

59)

(0.6

52)

(0.6

95)

(-0.

286)

(-0.

334)

(-0.

655)

(-0.

680

)(-

0.7

17)

(-0.

771)

(-0.

423

)(-

0.46

7)

Δ ln

be

ef t

-10.

617

0.6

210.

692

0.6

950.

448

0.4

510.

523

0.52

40.

438

0.43

80.

644

0.65

50.

549

0.55

50.

576

0.60

60.

367

0.37

20.

432

0.4

440.

608

0.6

150.

584

0.5

88

(3.0

56)*

*(3

.172

)**

(3.4

08)*

*(3

.422

)**

(2.6

37)*

*(2

.676

)**

(3.6

65)*

**(3

.712

)***

(3.3

55)*

**(3

.363

)***

(5.6

54)*

**(5

.690

)***

(4.2

63)*

**(4

.407

)***

(2.5

56)*

*(2

.752

)**

(2.4

37)*

*(2

.481

)**

(2.9

81)*

*(3

.117

)**

(4.2

58)*

**(4

.336

)***

(3.7

15)*

**(3

.743

)***

Δ ln

be

ef t

-20.

005

0.0

050.

006

0.0

070.

004

0.0

050.

002

0.00

20.

007

0.00

80.

005

0.00

50.

002

0.00

20.

004

0.00

50.

002

0.00

20.

001

0.0

030.

003

0.0

030.

002

0.0

04

(1.0

23)

(1.0

89)

(1.1

89)

(1.2

29)

(1.0

61)

(1.1

30)

(1.0

03)

(1.0

18)

(0.7

26)

(0.7

64)

(0.8

08)

(0.8

10)

(0.6

86)

(0.8

37)

(0.9

54)

(0.9

81)

(0.9

30)

(0.9

75)

(1.5

66)

(1.6

18)

(1.2

28)

(1.2

62)

(0.7

15)

(0.7

34)

Ob

serv

atio

ns

3315

3315

1530

1530

2550

2550

1170

1170

1950

1950

2106

2106

3315

3315

1530

1530

2550

2550

1170

011

7019

5019

5021

0621

06

Log

like

lih

oo

d-5

14.6

54-5

55.2

65-3

28.6

47-3

44.3

80-2

75.5

81-3

02.0

88-1

89.6

18-1

71.2

82-1

48.7

23-1

59.3

3519

6.73

6-2

19.4

81-5

89.2

44-5

25.4

67-3

69.4

13-3

91.5

82-3

37.9

11-3

15.3

90-1

73.9

30-1

78.5

10-1

56.9

77-1

57.6

5-2

39.2

92-2

46.6

29

Hau

sman

te

st0.

221

0.1

950.

089

0.1

020.

242

0.2

550.

075

0.07

10.

152

0.15

30.

179

0.18

5

(0.6

40)

(0.6

28)

(0.7

78)

(0.7

25)

(0.5

62)

(0.5

28)

(0.8

98)

(0.9

10)

(0.6

79)

(0.6

76)

(0.6

42)

(0.6

59)

EU25

EU27

1/20

07-6

/201

41/

2007

-6/2

014

EU25

EU27

1/20

07-6

/201

41/

2007

-6/2

014

EU25

1/20

05-6

/201

4

EU15

1/20

07-6

/201

4

MG

PM

G

EU15

1/20

05-6

/201

4

EU25

1/20

05-6

/201

4

EU15

1/20

07-6

/201

4

EU15

2/1

995-

6/2

014

EU15

2/19

95-6

/201

4

EU15

1/20

05-6

/201

4

Note

: *

**,

**,

* d

enot

e si

gni

fica

nce

at 1

%,

5% a

nd 1

0%,

resp

ecti

vely

. Aut

om

atic

sel

ecti

on

of

lags

bas

ed o

n A

kaik

e in

form

atio

n cr

iter

ion.

t-s

tati

stic

are

re

port

ed i

n p

aren

thes

es.

The

Hau

sman

tes

t is

a t

est

of p

ool

abil

ity

of t

he l

ong

-run

coe

ffic

ient

(i.

e. o

f th

e re

stri

ctio

n th

at a

ll c

ount

ries

hav

e th

e sa

me

long

-ru

n el

asti

city

). A

ll e

qua

tio

ns

incl

ude

a co

nsta

nt c

ount

ry-s

peci

fic

term

.

Tab

le 5

.10.

MG

an

d P

MG

reg

ress

ion

res

ult

s, d

epen

den

t va

riab

le:

lnbe

ef

78

tre

nd

no

tre

nd

tre

nd

no

tre

nd

tre

nd

no

tre

nd

tre

nd

no

tre

nd

tren

dn

o t

ren

dtr

end

no

tre

nd

tren

dn

o t

ren

dtr

en

dn

o t

ren

dtr

en

dn

o t

ren

dtr

en

dn

o t

ren

dtr

en

dn

o t

ren

dtr

end

no

tre

nd

Lon

g R

un

ln b

ee

f0.

091

0.09

20.

108

0.1

140.

067

0.0

710.

194

0.1

960.

136

0.1

420.

217

0.22

20.

066

0.06

70.

08

0.0

830.

042

0.0

460.

119

0.1

220.

099

0.1

030.

105

0.1

08

(1.7

90)

(1.8

48)

(1.7

36)

(1.8

12)

(1.3

04)

(1.5

16)

(3.7

75)*

**(3

.888

)***

(2.7

46)*

*(2

.851

)**

(3.8

82)*

**(4

.219

)***

(1.5

76)

(1.6

17)

(1.6

55)

(1.6

90)

(1.0

77)

(1.1

45)

(5.3

61)*

**(5

.552

)***

(3.1

83)*

*(3

.652

)***

(5.1

66)*

**(5

.648

)***

tim

e t

ren

d-0

.001

-0.0

02-0

.002

-0.0

01-0

.001

-0.0

03-0

.003

-0.0

03-0

.002

-0.0

02-0

.004

-0.0

06

(-1.

462)

(-1.

276

)(-

1.6

37)

(-1.

446

)(-

1.2

85)

(-2.

483

)**

(-1.

919

)*(-

2.0

27)*

(-1.

954

)*(-

1.2

28)

(-2.

734

)**

(-4.

203

)**

Sho

rt R

un

ECT

(ad

j. s

pee

d)

-0.0

24-0

.026

-0.0

45-0

.048

-0.0

33-0

.037

-0.0

53-0

.059

-0.0

72-0

.075

-0.0

81-0

.082

-0.0

2-0

.021

-0.0

38-0

.041

-0.0

27-0

.030

-0.0

51-0

.054

-0.0

66-0

.067

-0.0

74-0

.076

(-1.

480)

(-1.

507

)(-

2.72

6)**

(-2.

812

)**

(-1.

619

)(-

1.71

9)(-

4.1

20)*

**(-

4.27

5)**

*(-

6.7

33)*

**(-

6.84

6)**

*(-

8.1

91)*

**(-

8.2

19)*

**(-

1.22

3)(-

1.2

54)

(-1.

648)

(-1.

706

)(-

1.5

65)

(-1.

587)

(-2.

380)

**(-

2.5

82)*

*(-

2.8

28)*

*(-

2.90

1)**

(-5.

317

)***

(-5.

356

)***

Δ ln

be

ef

0.0

010.

001

0.0

020.

002

0.0

030.

004

0.02

30.

025

0.02

70.

027

0.0

280.

028

0.0

010.

001

0.0

030.

004

0.0

050.

006

0.00

50.

005

0.00

60.

007

0.0

070.

008

(0.6

92)

(0.7

44)

(1.0

12)

(1.0

78)

(1.5

99)

(1.6

23)

(2.8

80)*

*(2

.919

)**

(3.0

26)*

*(3

.047

)**

(3.2

19)*

*(3

.248

)**

(0.4

90)

(0.5

34)

(0.8

73)

(0.9

19)

(1.7

27)

(1.7

74)

(1.8

28)

(1.8

30)

(1.2

85)

(1.3

66)

(1.4

69)

(1.5

90)

Δ ln

be

ef t

-1-0

.001

-0.0

01-0

.001

-0.0

01-0

.001

-0.0

01-0

.008

-0.0

09-0

.01

-0.0

11-0

.004

-0.0

05-0

.001

-0.0

01-0

.001

-0.0

02-0

.002

-0.0

03-0

.005

-0.0

07-0

.008

-0.0

09-0

.007

-0.0

07

(-0.

755)

(-0.

785

)(-

0.5

67)

(-0.

582)

(-0.

753

)(-

0.76

0)(-

1.6

36)

(-1.

648)

(-2.

031)

*(-

2.1

14)*

(-1.

617)

(-1.

646

)(-

0.66

7)(-

0.7

38)

(-0.

944)

(-1.

010

)(-

1.7

59)

(-1.

828)

(-1.

953

)*(-

2.0

07)*

(-2.

186)

*(-

2.2

24)*

(-1.

955)

*(-

1.9

39)*

Δ ln

be

ef t

-20.

001

0.00

10.

001

0.0

010.

001

0.0

010.

001

0.0

010.

002

0.0

020.

002

0.00

20.

001

0.00

10.

001

0.0

010.

001

0.0

010.

001

0.0

010.

001

0.0

020.

003

0.0

03

(0.3

67)

(0.3

80)

(0.5

13)

(0.5

52)

(0.6

29)

(0.6

55)

(0.8

38)

(0.8

89)

(1.4

12)

(1.4

20)

(1.4

88)

(1.4

95)

(0.2

76)

(0.2

76)

(0.3

37)

(0.3

54)

(0.5

91)

(0.6

08)

(0.9

63)

(0.9

47)

(1.1

74)

(1.2

27)

(1.6

62)

(1.6

90)

Δ ln

po

rkt-

10.

559

0.56

70.

537

0.5

420.

396

0.4

060.

471

0.4

810.

522

0.5

290.

492

0.49

70.

518

0.52

00.

555

0.5

890.

351

0.3

540.

454

0.4

620.

479

0.4

830.

467

0.4

76

(5.5

39)*

**(5

.563

)***

(6.0

76)*

**(6

.137

)***

(4.1

64)*

*(4

.242

)**

(4.4

18)*

**(4

.555

)***

(5.4

18)*

**(5

.476

)***

(4.7

14)*

**(4

.661

)***

(6.0

30)*

**(6

.105

)***

(5.9

29)*

**(5

.959

)***

(4.8

18)*

**(4

.880

)***

(5.2

43)*

**(5

.316

)***

(5.6

97)*

**(5

.731

)***

(4.6

19)*

**(4

.641

***)

Δ ln

po

rkt-

20.

003

0.00

30.

004

0.0

030.

001

0.0

030.

002

0.0

020.

005

0.0

060.

006

0.00

60.

002

0.00

20.

003

0.0

030.

002

0.0

020.

001

0.0

020.

003

0.0

040.

003

0.0

04

(0.6

64)

(0.7

34)

(1.3

40)

(1.2

53)

(0.4

17)

(0.9

36)

(0.7

05)

(0.8

18)

(1.4

18)

(1.5

67)

(1.6

45)

(1.6

81)

(0.4

73)

(0.5

28)

(0.7

16)

(0.7

80)

(0.6

31)

(0.7

42)

(0.2

38)

(0.5

53)

(0.7

17)

(0.9

14)

(1.1

15)

(1.1

65)

Ob

serv

atio

ns

3315

3315

1530

1530

2550

2550

1170

1170

1950

1950

2106

2106

3315

3315

1530

1530

2550

2550

1170

011

7019

5019

5021

0621

06

Log

like

lih

oo

d-3

96.7

51-4

11.2

59-2

52.6

74-2

70.1

25-1

98.6

47-2

13.3

46-1

45.7

82-1

61.8

38-1

40.2

38-1

58.6

39-2

01.9

61-2

22.3

77-4

10.6

93-4

33.5

84-2

95.6

42-3

16.1

98-3

37.5

25-3

54.6

12-1

68.7

67-1

73.9

73-1

55.9

51-1

71.3

60-2

07.6

91-2

19.6

34

Hau

sman

te

st0.

071

0.07

50.

042

0.0

430.

035

0.0

380.

055

0.0

590.

092

0.0

950.

101

0.10

5

(0.9

10)

(0.9

02)

(0.9

58)

(0.9

55)

(0.9

72)

(0.9

68)

(0.9

29)

(0.9

22)

(0.7

95)

(0.7

83)

(0.7

30)

(0.7

22)

1/2

005-

6/2

014

1/2

007-

6/2

014

EU25

EU27

1/20

07-6

/201

41/

2007

-6/2

014

EU25

EU15

2/1

995-

6/20

14

EU25

1/2

005-

6/2

014

1/2

005-

6/2

014

1/20

07-6

/201

4

EU27

1/20

07-6

/201

41/

2007

-6/2

014

EU15

EU15

EU25

EU15

EU15

2/1

995-

6/20

141/

200

5-6/

2014

EU15

MG

PM

G

Tab

le 5

.11.

MG

an

d P

MG

reg

ress

ion

res

ult

s, d

epen

den

t va

riab

le:

lnpo

rk

Note

: *

**,

**,

* de

note

sig

nif

ican

ce a

t 1%

, 5%

and

10%

, re

spec

tive

ly. A

uto

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ic s

elec

tio

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f la

gs b

ased

on

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ike

info

rmat

ion

crit

erio

n. t

-st

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tic

are

rep

orte

d i

n p

aren

thes

es.

The

Hau

sman

tes

t is

a t

est

of

poo

labi

lity

of

the

long

-run

coe

ffic

ient

(i.

e. o

f th

e re

stri

ctio

n t

hat

all

coun

trie

s ha

ve

the

sam

e lo

ng-r

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ty).

All

equ

atio

ns i

ncl

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a co

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nt c

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try-

spec

ific

ter

m.

79

Overall, our baseline results in Table 5.10 seem to be relatively robust: there is long run

equilibrium between pork and beef prices. Pork price is a significant factor affecting

beef price in the EU in long run. Since variables are defined in logarithmic terms, the

estimated coefficients are directly the price elasticities of beef price with respect to pork

price. All MG and PMG regressions results in Table 5.10 show a significant and positive

long run coefficient. On the contrary, the magnitude of the long-term coefficient is

affected by the estimation method. By choosing the MG rather than the PMG method

slightly higher values are obtained. We employ the Hausman test of long-run

homogeneity restriction to choose the appropriate estimator. Namely, homogeneity of

long run coefficients implied by PMG estimating procedure cannot be assumed a priori

but needs to be tested. When long-run homogeneity restriction can be accepted, PMG

estimates would be more efficient compared to MG. Respectively, when the slope

coefficients are heterogeneous, then PMG estimates would be inconsistent and the MG

estimator provides consistent estimates of the mean of long-run coefficients. According

to test results shown at the bottom of Table 5.10, we cannot reject the null of long-run

homogeneity restriction, implying that the PMG estimator is efficient under the null

hypothesis and is preferred over the MG estimator.

We can now concentrate on the estimation results of PMG estimation method. The PMG

estimates find strong evidence of positive relationship between the EU pork and beef

prices. According to the results, there exists cross-commodity price transmission from

pork prices to beef prices in the EU. All PMG regressions results in Table 5.11 show a

significant and positive coefficient of 0.15 to 0.33 in the long run, implying that a 1%

increase in pork prices, ceteris paribus, would raise beef prices 0.15-0.33%. According

to the results of PMG estimation the relationship between the two price series is the

strongest and, thus, the price transmission is the most significant in the old member

states, EU15 countries. Also, in the case of EU15, we notice that the price transmission

is more apparent and the coefficients are higher when we investigate estimation results

from subperiod 1/2007-6/2014 compared to estimation results from subperiod 1/2005-

6/2014 or from the full period 2/1995-6/2014. This indicates that integration of the EU

livestock market of pork and beef has increased during the investigation period. This is

also valid when analyzing price transmission and market integration between pork and

beef in the EU25 countries: the significance of long run coefficient is higher and the

long run price elasticity measured by the estimated long run coefficient is larger in

estimation results from subperiod 1/2007-6/2014 than in estimation results from the full

period 1/2005-6/2014.

The estimated error-correcting speed adjustment term ECT is, as expected, significantly

negative and less than 1 in absolute value, implying convergence towards long run

equilibrium relationship. The lowest value of the error correction coefficient in the PMG

estimation results is 0.035 and the highest is 0.093, implying a speed of adjustment of

80

about 1-2 years. The speed of adjustment has also improved during the investigation

period; In the case of EU15 countries, the adjustment term is 0.056 (trend)/0.060 (no

trend) in the estimation results from the period 1/2005-6/2014 and 0.086 (trend) 0.093

(no trend) in the results from the period 1/2007-6/2014. This means that after the year

2005 the convergence to the equilibrium has sped up from 1.5 years to 1 year. Price

transmission between the EU meat markets has fastened significantly but is still quite

slow. This indicates that there exist some rigidities that decelerates the price transmis-

sion process.

Furthermore, we can note that in short run the variation in pork prices has affected beef

prices only after 2007. This means that short run relationship between pork and beef

prices has only existed since 2007. Moreover, short term price transmission from pork

price to beef price in the EU is interestingly negative according to our PMG estimation

results. This means that increase in pork price lower the beef price in short term.

However, this short term effect is minor although statistically significant.

By repeating the estimation of the model for EU15 in period 1/2005-6/2014 and for

EU15 and EU25 in period 1/2007-6/2014 we also check the sensitivity of the results at

the type of countries included in the panel; i.e. we check what kind of influence the new

member states have had on the results. We notice that the panel estimates indicate cross-

sectional stability over results. The inclusion of the new member states to the data has

not changed the interpretation of the estimation results.

In table 5.11 we repeat the estimation but now we specify pork as a dependent variable

and beef as an independent variable. Consequently, we are interested in the price

elasticities of pork with respect to beef price. The long-run slope homogeneity

hypothesis of PMG is tested via the Hausman test. On the basis of the Hausman test it is

not possible to reject the hypothesis of poolability of the long-run elasticity of the beef

price and we can therefore focus on the results of PMG estimation method. All in all,

MG estimation method provides larger coefficients than PMG method. According to the

results of PMG estimation, there exists cross-commodity price transmission in long run

from beef prices to pork prices in the EU only in the estimation results from subperiod

1/2007-6/2014. This can be interpreted as overall increased integration and efficiency in

livestock market in the EU during the past ten years; not only the variation of pork price

will transmit to the prices of other meat types but also vice versa. There is bi-directional

relationship between pork and beef prices in the EU in long run although price

transmission coming from the fluctuations in pork price is larger and more significant.

Notably, this bi-directional relationship has not been valid until 2007 in our investiga-

tion. In table 5.11 the estimation results from subperiod 1/2007-6/2014 show a

significant and positive coefficient of 0.099 to 0.122 in the long run, implying that a 1%

increase in beef price ceteris paribus, would raise pork price 0.1-0.12% in the EU.

81

The error correction terms are negative and significant; hence the null hypothesis of no

long-run relationship is rejected. According to the results of PMG estimation, the speed

of adjustment from the deviation in the long run relationship between the beef and pork

prices is between -0.051 and -0.076. The model implies moderate adjustment inertia; it

converges to the equilibrium, with 5-7.5% percent of discrepancy corrected in each

period. No significant price transmission from beef price to pork price is found in short

run in the EU livestock market. Overall, this suggests that short-run dynamics is

significantly different in livestock market compared to long run dynamics.

5.4 Robustness check against estimation method

Next, we use alternative heterogeneous panel cointegration techniques that correct for

endogeneity, namely Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS). We

use FMOLS and DOLS between-dimension estimators (Group Mean Estimator)

proposed by Pedroni (2001). Unlike the PMG, which uses a maximum likelihood

method, FMOLS and DOLS are based on a modified OLS26. FMOLS and DOLS

estimators allow us to relax the assumption of long-run homogeneity. Both methods

allow for regressors’ complete endogeneity, and treat all parameters, i.e. dynamics and

cointegrating vectors, as heterogeneous across panel members. FMOLS runs a static

OLS with fixed effects for each panel member individually and uses the estimated

residuals to (non-parametrically) build member-specific adjustment terms, which are

then used to correct for each member’s endogeneity. Differences in regressors are used

as internal instruments. Instead, DOLS individually corrects for endogeneity

parametrically by running OLS with fixed effects for each panel member including

leads and lags of differenced regressors.

It is worth to note that FMOLS and DOLS are static models and thus, we can only

estimate long term relationship between pork price and beef price in the EU member

states.

The DOLS estimates employ two lags and two leads. Overall, the results in Table 5.12

are robust with respect to the choice of the lag structure. A country-specific constant has

been incorporated while a time trend was not included.

26 It is worth noting that under endogeneity and no cointegration, using OLS produces first-order bias, and external instruments are needed. However, under endogeneity and cointegration, OLS produces superconsistent estimates and second-order endogeneity bias (i.e. inconsistent estimates of standard errors). Internal instruments are then used (such as in FMOLS and DOLS).

82

These results of DOLS and FMOLS estimates confirm the existence of a long-run

relationship between pork and beef prices in the EU countries. All the long run

coefficients obtained by applying FMOLS and DOLS have the same signs although

higher magnitudes as the previous PMG results, suggesting equilibrium derived from

the PMG method are roughly equal to those from FMOLS and DOLS. The results from

FMOLS and DOLS estimation also confirm the observation that price transmission and

market integration has increased between pork market and beef market in the EU during

FMO

LSD

OLS

FMO

LSD

OLS

FMO

LSD

OLS

FMO

LSD

OLS

FMO

LSD

OLS

FMO

LSD

OLS

Ind

epe

nd

en

t va

ria

ble

: ln

po

rk

ln b

eef

0.1

640

.203

0.1

49

0.1

65

0.1

32

0.1

720

.333

0.3

610

.311

0.3

570

.37

50

.39

0

(7.4

53

)***

(9.0

48)*

**(6

.761

)***

(8.8

19)*

**(6

.520

)**

*(7

.476

)**

*(1

3.0

62)*

**(

14

.281

)***

(12

.373

)***

(12.

98

2)*

**(1

5.2

07

)***

(15.

916

)***

Ob

serv

ati

ons

331

53

315

153

01

530

25

50

25

50

117

01

170

195

01

950

210

62

106

Ind

epe

nd

en

t va

ria

ble

: ln

be

ef

ln p

ork

0

.109

0.1

310

.12

70

.15

60

.11

90

.123

0.2

880

.312

0.1

890

.216

0.2

31

0.2

68

(2.4

59

)**

(3.1

09)*

**(3

.061

)***

(3.7

95)*

**(2

.227

)**

*(2

.258

)**

*(7

.136

)***

(7.7

88

)***

(5.1

61

)***

(5.9

07

)***

(6.3

44)*

**(6

.765

)***

Ob

serv

ati

ons

331

53

315

153

01

530

25

50

25

50

117

01

170

195

01

950

210

62

106

1/2

007-

6/2

014

EU1

5E

U1

5EU

25

EU1

5EU

25

EU2

7

2/1

995-

6/2

014

1/2

005-

6/2

014

1/2

005

-6/2

01

41

/200

7-6

/201

41

/200

7-6

/201

4

Not

e:

***,

**,

* d

eno

te s

igni

fica

nce

at 1

%, 5

% a

nd 1

0%, r

espe

ctiv

ely.

t-st

atis

tic

are

rep

orte

d i

n pa

rent

hese

s.

Tab

le 5

.12.

FM

OL

S a

nd

DO

LS

reg

ress

ion

res

ult

s

83

the observation period. In addition these estimation results confirm the cross-sectional

stability: results are not alert the type of countries included in the panel (EU15, EU25 or

EU27).

5.5 Robustness check against definitions of the data sample

In this section, the robustness of the relation between pork and beef price series is

checked against alternative definitions of the data sample. We address the following

questions. How is the relation changed over time? Are there countries with a

significantly different behavior?

We first check the stability of our results over time via PMG estimation. The entire

estimation period of 2/1995-6/2014 is divided into 4 subperiods (2/1995-12/1999,

1/2000-12/2004, 1/2005-12/2009, 1/2010-6/214) and estimation is repeated for every

subperiod including EU15 member states. These estimation results are presented in

Appendix 2.

The estimation results in Tables 5.10 and 5.11 already revealed that integration in the

EU livestock market of pork and beef has increased during the investigation period. The

purpose of this robustness check is to get a better picture of this development and also

examine the meat market integration and cross-commodity price transmission between

pork and beef during the early stage of the EU.

The estimation results show that the long-term elasticity and the speed adjustment term

have changed substantially between the subperiods considered. Overall, estimations

suggest a significantly different and higher relation between price series for the latter

subperiods. We can observe that EU meat market was diverged before year 2005. It

cannot be found statistically significant relation between pork and beef prices during the

periods 2/1995-12/1999 and 1/2000-12/2004. Since 2005, however, EU meat market

integration has increased sharply and price transmission between meat markets has

become significant. This is true in both cases: when we have lnpork as an independent

variable and when we have lnbeef as an independent variable. This is clear evidence that

meat market in Europe is well functioned and efficient and the direction of the EU

livestock market is towards stronger integration.

Next, we proceed by studying robustness of our results across countries. The findings

from our estimation might be affected also by the relative small number of countries in

the data sample. As a further robustness check we have re-estimated the model via PMG

estimation method excluding from the data one country at a time. This permits to

84

understand whether the results are strongly driven the behavior of a single country.

Figures in Appendix 3 plot the value of the long-run elasticity and speed adjustment

term on the country excluded from the sample.

According to the data availability, there are figures for EU15 member states, EU25

member states (excl. EU15) and EU27 member states (excl. EU25). In the first set of

figures independent variable is lnbeef. In the latter set lnpork is treated as an

independent variable.

In the first figures, EU15 countries that appear to influence significantly on the

estimation of the long-run elasticity are Denmark, Germany, Greece, Netherlands and

Portugal. When Denmark, Germany, and Netherlands are excluded from the sample, the

estimated elasticity is significantly lower: values of the long-run elasticity falling

outside the 95% confidence band estimated from the whole sample. This indicates that

the presence of these countries contributes to keep high the value of the long-run

elasticity estimated on the whole sample. Furthermore, these results can also be

interpreted that Denmark, Germany and Netherlands have the most significant influence

on the meat market integration and meat price transmission in the EU. Without these

countries EU meat market would not be as integrated and as efficient. They are also the

leaders in the EU pork market. According to the results, Germany is the leader when we

consider effectiveness of the meat market measured by the speed of price transmission

(responses are the fastest)27 but Denmark is the price leader which has the greatest

impact on the cross-commodity price transmission in the EU meat market (responses are

the largest)28.

Respectively, Greece and Portugal appears to decrease significantly the value estimated

across the whole panel of EU15 member states: the exclusion leads to an estimated

elasticity of about 0.22, while the estimated elasticity of the whole panel data sample is

0.197. This also means that Greece and Portugal have negative effect on meat market

integration in the EU and their inclusion reduces effectiveness of the EU meat market.

When we focus on new member states, inclusion of Cyprus, Slovakia, Slovenia and

Bulgaria has also statistically significant negative influence on transmission between

pork and beef prices and on the meat market integration in the EU.

When considering the speed of adjustment terms the values of variables come back to

the long-run equilibrium level Denmark and Germany have the most significant

influence on the meat market integration. When they are excluded from the data sample,

27 The estimated speed of adjustment term is the lowest when Germany is excluded from the data. 28 The estimated long-run elasticity is the lowest when Denmark is excluded from the data.

85

the estimated speed of adjustment term is statistically significantly lower. Greece has

opposite influence. In the figures on the speed of adjustment terms covering EU25

member states (excl. EU15), Latvia and Lithuania have statistically significant negative

effect on the meat market integration. When they are excluded from the sample, the

estimated speed of adjustment term is significantly higher.

In the latter set of figures, lnpork is treated as an independent variable. According to the

results, EU15 countries influencing statistically significantly on the estimation results of

the long-run elasticity are France, Germany, Greece, Ireland, and the UK. Presence of

France, Germany, Ireland and the UK contributes to keep high the value of the long-run

elasticity estimated on the whole sample. When they are one at time excluded from the

data, the estimated coefficients of long-run elasticity fall outside the 95% confidence

band. Without these countries price transmission from beef to pork in the EU would not

be as large and as efficient. They appear to be the leaders in the EU beef market.

Presence of Greece in the data sample affects another direction: Greece has negative

effect on meat market integration in the EU and its inclusion reduces effectiveness of

the EU meat market. When we focus on the new member states, inclusion of Slovakia

and Slovenia has statistically significant negative influence on price transmission from

beef to pork and on the meat market integration among the EU25 member states.

When lnpork is an independent variable and lnbeef is an explanatory variable, the

estimated speed of adjustment term is significantly higher when Greece, Cyprus,

Solavakia, Slovenia and Bulgaria are one by one excluded from the data set. France,

Germany, Ireland and the UK have instead opposite influence and they have statistically

significant positive effect on the meat market integration when we are considering price

transmission from beef prices to pork prices.

All in all, the impacts, though significant, appear to be quite moderate and they are not

strong enough to alter qualitative results. Moreover, they do not alter considerably

quantitative results. We may conclude that our results are robust against estimation

method and also against data sample definition.

86

6 CONCLUDING REMARKS

The main objective of the study is to analyze the dynamics of price transmission and

market integration in the EU livestock market of pork and beef. Our focus is on

horizontal cross-commodity price transmission which is not as common approach as

vertical or spatial price transmission. However, it provides us interesting information on

the connection between commodity prices and integration of commodity markets.

We utilize recently developed panel time-series techniques to analyze the price linkages

in the EU meat sector. Our data consists of monthly data on pork and beef prices in the

EU member countries during the period from February 1995 to June 2014.

The results indicate that there exists bi-directional relationship between pork and beef

prices in the EU in long run. According to the estimation results price transmission is

the most significant in the old member states, EU15 countries. Price transmission

coming from the fluctuations in pork price is larger and more significant than price

transmission coming from the fluctuations in beef price. Depending on investigation

period, 1% increase in pork prices, ceteris paribus, would raise beef prices 0.15-0.33%.

Long run price transmission from beef price to pork price can be observed only in the

results from subperiod 1/2007-6/2014. These results imply that a 1% increase in beef

price ceteris paribus, would raise pork price 0.1-0.12% in the EU.

Cross-commodity price transmission between pork and beef has increased remarkably

during the past ten years in the EU15 member states. It cannot be found statistically

significant relation between pork and beef prices before year 2005. However, ever since

EU meat market integration has increased sharply and price transmission between meat

markets has become significant. This is also valid when analyzing price transmission

and market integration between pork and beef in the EU25 and EU27 countries. This

indicates that integration of the EU livestock market of pork and beef has increased

during the investigation period.

Also the convergence to the equilibrium has sped up. In the case of EU15 countries after

the year 2005 the convergence to the equilibrium has sped up from roughly 1.5 year to 1

year. Although the equilibrium is achieved faster than before, the convergence process is

still quite slow. Price transmission from pork to beef in the EU livestock market is

87

strong in magnitude but there still exist some rigidities that decelerates the process on

the whole.

In short run, we found evidence only for price transmission from pork prices to beef

prices in the EU, not vice versa. Short run relationship between pork and beef prices has

only existed since 2007. Moreover, according to the estimation results, short term price

transmission from pork price to beef price is interestingly negative. This means that

increase in pork price lower the beef price in short term. Overall, short-run dynamics is

significantly different in the EU livestock market compared to long run dynamics. This

can partly be explained by different time lags in the adaptation of markets. The

inclusion of the new EU member states to the data has not changed the interpretation of

the estimation results.

We also checked whether the results are strongly driven the performance of a single

country. The results indicate that Danish, German and Dutch pork markets and Irish,

French, German and British beef markets have the most significant influence on the

meat market integration and meat price transmission in the EU. Without these countries

EU meat market would not be as integrated and as efficient. Denmark, Germany and

Netherlands are also the leaders in the EU pork market. Similarly, Ireland, France,

Germany and the UK appear to be the leaders in the EU beef market. Countries that

have negative effect on meat market integration in the EU are most clearly Greece,

Cyprus, Slovakia, Slovenia and Bulgaria. However, the impacts caused by the

significance of meat market of a single country, appear to be moderate – though

statistically significant – and they do not alter our main results.

Based on our empirical results we can conclude that the EU livestock market is

interactional and integrated. The meat market integration in the EU has proceeded

rapidly in recent years. Reduction of barriers to the trade of agricultural products has

made the integration possible. Furthermore, it is still important to ensure that regulation

measures of the Union treat all member state equally: prevent the barriers to trade

between the member countries, harmonize the conditions of competition and monitor

that common rules are widely abided by as they stand.

Today, operational environment of the EU meat market is characterized by rapid and

significant price fluctuations. The major price fluctuations are affecting increasingly

operation of the meat market. The integration of meat market has intensified at the same

time as the price fluctuations have generalized. Consequently, this means that the

market integration has promoted the price fluctuations to spread between product

markets. As the market and price risks grow, managing them is crucial in the EU

agriculture policy.

88

From the point of view of the market integration, the EU meat market is well-functioned

and effective. The impacts of the policy measures go faster and faster through the whole

meat market. Due to the price integration, targeted policy measures for the one branch

of the meat market has impact on the other meat market branches too. Policy decisions

concerning the EU meat market must be designed by the perspective of the performance

of the whole meat market.

89

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96

Appendix 1. LM unit root test results for individual EU member states

assuming one structural breakpoint.

Note: ***, **, * denote significance at 1%, 5% and 10%, respectively.

Note: ***, **, * denote significance at 1%, 5% and 10%, respectively.

Univariate LM

Stat Optimal Lag Break Location

Univariate LM


Univariate LM


Austria -4.987** 6 10/2004 -6.451*** 4 3/2011 -6.623*** 3 3/2011

Belgium -6.212*** 5 12/2003 -7.397*** 3 9/2012 -8.055*** 3 9/2012

Denmark -8.758*** 3 2/2001 -10.113*** 3 10/2010 -12.438*** 3 10/2010

Finland -7.055*** 2 7/2001 -8.656*** 2 1/2011 -9.174*** 2 1/2011

France -6.826*** 8 4/2003 -7.904*** 6 7/2010 -7.843*** 5 7/2010

Germany -8.492*** 4 3/2001 -9.649*** 2 4/2011 -10.963*** 2 4/2011

Greece -3.367 7 5/2006 -4.007* 5 2/2013 -3.898* 4 2/2013

Ireland -5.813*** 5 8/2003 -4.937*** 5 5/2012 -5.674*** 5 5/2019

Italy -4.064* 5 6/2005 -4.711** 5 7/2012 -6.058*** 4 7/2012

Luxembourg -5.824*** 7 9/2003 -5.868*** 6 11/2012 -7.187*** 4 11/2012

Netherlands -9.142*** 3 6/2001 -10.674*** 4 3/2011 -10.008*** 4 3/2011

Portugal -3.548 6 5/1998 -4.216* 3 1/2013 -5.286*** 3 1/2013

Spain -5.371** 5 10/2000 -6.542*** 4 6/2012 -6.131*** 4 6/2012

Sweden -6.809*** 5 3/2002 -7.396*** 2 8/2011 -7.770*** 2 8/2011

United Kingdom -7.471*** 7 11/2001 -9.060*** 3 1/2012 -8.898*** 3 1/2012

Cyprus -5.112*** 6 12/2010 -5.973*** 5 12/2010

Czech Republic -6.087*** 7 12/2009 -6.244*** 5 12/2009

Estonia -6.596*** 5 4/2011 -6.111*** 4 4/2011

Hungary -3.859* 5 2/2011 -3.563 1 2/2011

Latvia -3.635* 4 9/2009 -4.502** 4 9/2009

Lithuania -4.738** 3 1/2010 -5.562*** 3 1/2010

Poland -6.953*** 4 10/2010 -7.324*** 4 10/2010

Slovakia -5.044** 6 7/2010 -5.129** 5 7/2010

Slovenia -5.729*** 1 3/2011 -6.376*** 3 3/2011

Bulgaria -6.275*** 2 3/2012

Romania -4.996*** 3 5/2013

PORK

EU15 (2/1995-6/2014) EU25 (1/2005-6/2014) EU27 (1/2007-6/2014)

Univariate LM


Univariate LM


Univariate LM


Austria -5.893*** 4 2/2000 -7.045*** 3 10/2010 -7.545*** 4 10/2010

Belgium -7.114*** 3 3/2003 -8.205*** 3 5/2011 -8.803*** 4 5/2011

Denmark -6.252*** 3 9/2001 -6.989*** 3 3/2013 -11.806*** 2 3/2013

Finland -5.387*** 5 2/2002 -6.364*** 3 1/2012 -10.532*** 3 1/2012

France -3.071 4 9/2009 -3.552 4 8/2012 -3.683 1 8/2012

Germany -7.646*** 3 6/2001 -7.898*** 4 5/2012 -8.199*** 4 5/2012

Greece -2.640 5 5/2005 -2.877 4 1/2007 -2.898 4 1/2007

Ireland -5.257*** 5 2/2004 -4.763** 5 6/2011 -5.094*** 5 6/2011

Italy -3.165 3 9/2003 -3.332 5 11/2011 -3.541 4 11/2011

Luxembourg -5.764*** 6 11/2003 -6.286*** 3 1/2013 -6.687*** 4 1/2013

Netherlands -7.259*** 2 2/2002 -7.648*** 2 9/2011 -8.190*** 3 9/2011

Portugal -5.863*** 3 3/2004 -5.905*** 2 2/2008 -5.673*** 5 2/2008

Spain -3.617 6 8/2004 -3.070 4 4/2013 -3.486 5 4/2013

Sweden -5.836*** 3 7/2007 -6.450*** 1 6/2011 -7.117*** 2 6/2011

United Kingdom -8.451*** 5 1/2008 -7.937*** 4 2/2012 -7.493*** 4 2/2012

Cyprus -6.491*** 5 9/2007 -6.832*** 6 9/2007

Czech Republic -7.514*** 5 10/2009 -8.091*** 4 10/2009

Estonia -5.284*** 5 2/2006 -5.333*** 2 11/2012

Hungary -2.525 5 1/2011 -2.661 3 1/2011

Latvia -3.061 6 11/2011 -2.864 4 11/2011

Lithuania -3.357 4 9/2010 -3.070 6 9/2010

Poland -5.153*** 4 11/2010 -5.486*** 5 11/2010

Slovakia -4.266** 6 1/2011 -4.962*** 5 1/2011

Slovenia -4.189** 5 12/2009 -4.221** 1 12/2009

Bulgaria -7.920*** 4 1/2010

Romania -5.873*** 3 9/2007

BEEF

EU15 (2/1995-6/2014) EU25 (1/2005-6/2014) EU27 (1/2007-6/2014)

97

Appendix 2. Pooled Mean Group ECM estimates, EU15 over different

sub-periods

Independent variable: lnbeef

Note: ***, **, * denote significance at 1%, 5% and 10%, respectively. Automatic selection of lags based on Akaike information criterion. t-statistic are reported in parentheses. All equations include a constant country-specific term.

Independent variable: lnpork

Note: ***, **, * denote significance at 1%, 5% and 10%, respectively. Automatic selection of lags based on Akaike information criterion. t-statistic are reported in parentheses. All equations include a constant country-specific term.

trend no trend trend no trend trend no trend trend no trend

Long Run

ln pork 0.121 0.128 0.145 0.153 0.288 0.296 0.315 0.324

(1.173) (1.228) (1.692) (1.809) (24.582)*** (25.074)*** (38.616)*** (39.445)***

time trend 0.001 0.002 0.003 0.005

(1.556) (2.724)** (3.883)*** (4.690)**

Short Run

ECT (adj. speed) 0.042 0.044 -0.068 -0.069 -0.183 -0.190 -0.204 -0.209

(0.791) (0.836) (-1.567) (-1.615) (-20.770)***(-22.181)***(-21.338)***(-21.919)***

Δ ln pork 0.002 0.002 0.001 0.002 0.022 0.022 0.025 0.027

(0.660) (0.691) (1.072 (1.109) (3.136)** (3.123)** (3.395)*** (3.417)***

Δ ln porkt-1 -0.001 -0.001 -0.001 -0.001 0.009 -0.008 0.010 0.012

(-0.553) (-0.573) (-0.817) (-0.829) (2.850)** (2.866)** (3.125)** (3.157)**

Δ ln porkt-2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.517) (0.506) (0.584) (0.582) (0.749) (0.720) (1.019) (1.033)

Δ ln beeft-1 0.456 0.462 0.604 0.611 0.531 0.535 0.489 0.492

(4.125)*** (4.201)*** (5.228)*** (5.321)*** (4.919)*** (5.074)*** (5.461)*** (5.547)***

Δ ln beeft-2 0.009 0.009 0.010 0.009 0.004 0.004 0.008 0.006

(2.081)* (2.063)* (2.314)** (2.276)** (1.871) (1.850) (1.929)* (1.818)

Observations 885 900 900 900 900 900 810 810

Log likelihood -102.541 -89.578 -125.673 -100.284 -222.548 -208.649 -267.590 -238.181

2/1995-12/1999 1/2000-12/2004 1/2005-12/2009 1/2010-6/2014

trend no trend trend no trend trend no trend trend no trend

Long Run

ln beef 0.051 0.053 0.072 0.076 0.109 0.112 0.137 0.142

(0.890) (0.932) (1.257) (1.295) (1.945)* (2.163)** (5.368)*** (5.559)***

time trend -0.005 -0.005 -0.002 -0.002

(-3.118)** (-2.926)** (-1.647) (-1.779)

Short Run

ECT (adj. speed) 0.014 0.016 -0.004 -0.004 -0.073 -0.077 -0.110 -0.112

(0.135) (0.191) (0.813) (0.866) (-4.614)*** (-4.658)*** (-9.229)*** (-9.289)***

Δ ln beef 0.001 0.001 0.001 0.001 0.010 0.009 0.017 0.020

(0.410) (0.448) (0.382) (0.402) (2.375)** (2.327)** (3.456)** (3.713)***

Δ ln beeft-1 -0.001 -0.001 -0.001 -0.001 -0.003 -0.003 -0.006 -0.008

(-0.103) (-0.125) (-0.187) (-0.218) (-0.591) (-0.644) (-1.226) (-1.232)

Δ ln beeft-2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

(0.151) (0.108) (0.219) (0.252) (0.723) (0.764) (1.513) (1.567)

Δ ln porkt-1 0.701 0.708 0.722 0.731 0.687 0.689 0.743 0.752

(9.147)*** (9.306)*** (10.148)***(10.135)*** (8.990)*** (8.981)*** (9.357)*** (9.456)***

Δ ln porkt-2 0.005 0.005 0.004 0.004 0.002 0.003 0.004 0.004

(0.747) (0.793) (1.156) (1.188) (0.917) (0.944) (1.362) (1.381)

Observations 885 900 900 900 900 900 810 810

Log likelihood -77.890 -90.261 -82.696 -98.987 -131.254 -140.383 -144.229 -153.627

2/1995-12/1999 1/2000-12/2004 1/2005-12/2009 1/2010-6/2014

98

Appendix 3. Cross-sectional stabilities of the long-run elasticity and

the speed of adjustment

EU15 (period: 2/1995-6/2014)

lnbeef as an independent variable

Long-run elasticity

Speed of adjustment

0,12

0,14

0,16

0,18

0,20

0,22

0,24

Aust

ria

Belg

ium

Denm

ark

Fin

land

Fra

nce

Germ

any

Gre

ece

Irela

nd

Italy

Luxe

mbourg

Neth

erl

ands

Port

ug

al

Spain

Sw

ede

n

Unite

d K

ingdom

-0,042

-0,040

-0,038

-0,036

-0,034

-0,032

Au

str

ia

Be

lgiu

m

De

nm

ark

Fin

lan

d

Fra

nce

Ge

rma

ny

Gre

ece

Ire

lan

d

Ita

ly

Lu

xe

mb

ou

rg

Ne

the

rla

nd

s

Po

rtu

ga

l

Sp

ain

Sw

ed

en

Un

ite

d K

ing

do

m

99

EU25 (excl. EU15; period: 1/2005-6/2014)


Long-run elasticity

Speed of adjustment

0,10

0,12

0,14

0,16

0,18

0,20

Cyp

rus

Cze

ch R

epublic

Est

onia

Hunga

ry

Latv

ia

Lith

uania

Pola

nd

Slo

vaki

a

Slo

ven

ia

-0,060

-0,058

-0,056

-0,054

-0,052

-0,050

-0,048

Cyp

rus

Cze

ch

Re

pu

blic

Esto

nia

Hu

ng

ary

La

tvia

Lith

ua

nia

Po

lan

d

Slo

va

kia

Slo

ve

nia

100

EU27 (excl. EU25; period: 1/2007-6/2014)


Long-run elasticity

Speed of adjustment

0,20

0,22

0,24

0,26

0,28

0,30

0,32

0,34

Bulgaria Romania

-0,096

-0,094

-0,092

-0,090

-0,088

-0,086

-0,084

-0,082

-0,080

Bulgaria Romania

101

EU15 (period: 2/1995-6/2014)

lnpork as an independent variable

Long-run elasticity

Speed of adjustment

0,050

0,055

0,060

0,065

0,070

0,075

Aust

ria

Belg

ium

Denm

ark

Fin

land

Fra

nce

Germ

any

Gre

ece

Irela

nd

Italy

Luxe

mbourg

Neth

erl

ands

Port

ug

al

Spain

Sw

ede

n

Unite

d K

ingdom

-0,026

-0,024

-0,022

-0,02

-0,018

-0,016

Au

str

ia

Be

lgiu

m

De

nm

ark

Fin

lan

d

Fra

nce

Ge

rma

ny

Gre

ece

Ire

lan

d

Ita

ly

Lu

xe

mb

ou

rg

Ne

the

rlan

ds

Po

rtu

ga

l

Sp

ain

Sw

ed

en

Un

ite

d K

ing

do

m

102

EU25 (excl. EU15; period: 1/2005-6/2014)


Long-run elasticity

Speed of adjustment

0,035

0,040

0,045

0,050

0,055

Cyp

rus

Cze

ch

Re

pu

blic

Esto

nia

Hu

ng

ary

La

tvia

Lith

ua

nia

Po

lan

d

Slo

va

kia

Slo

ve

nia

-0,034

-0,032

-0,03

-0,028

-0,026

Cyp

rus

Cze

ch

Re

pu

blic

Esto

nia

Hu

ng

ary

La

tvia

Lith

ua

nia

Po

lan

d

Slo

va

kia

Slo

ve

nia

103

EU27 (excl. EU25; period: 1/2007-6/2014)


Long-run elasticity

Speed of adjustment

0,095

0,100

0,105

0,110

0,115

Bulgaria Romania

-0,090

-0,085

-0,080

-0,075

-0,070

-0,065

Bulgaria Romania

104

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Dividend Decision under Dual Income Taxation 21. Satu Nivalainen. 2010. Essays on family migration and geographical mobility in

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