modelling export equations using an unobserved component … export... · tigations through the...

Empir Econ (2011) 41:593–637DOI 10.1007/s00181-010-0399-y

Modelling export equations using an unobservedcomponent model: the case of the Euro Areaand its competitors

Bernardina Algieri

Received: 29 April 2008 / Accepted: 17 June 2010 / Published online: 26 September 2010© Springer-Verlag 2010

Abstract This article analyses developments in the external sector for the EuroArea and its major competitors and quantifies the dynamic contributions of the keydeterminants of trade to export volume behaviour. In addition to the traditional vari-ables affecting export volumes, price and foreign demand, an unobserved componentin the form of a time-varying trend enters the export equations to capture underlyingnon-price competitiveness. The structural modelling approach used within an errorcorrection framework allows isolating the different sources of trade fluctuations andto better assess the contribution of each set of variables to export flows. The findingsconfirm that stochastic trends are present as a result of technical change and otherexogenous factors driving export flows, and that a failure to account for these trendswill lead to biased estimates of long-run price elasticities.

Keywords Exports · Stochastic trends · Price elasticities · ECM

JEL Classification C32 · C51 · F14

1 Introduction

Over the last 25 years, numerous empirical studies on trade have been devoted to theestimation of import and export equations. The traditional models of trade specify twokey determinants of export flows: price competitiveness and foreign demand.1 Empir-ical evidence and alternative theories suggest, however, that the two variables alone do

1 See Goldstein and Khan (1985), Hooper and Mann (1989), Hooper and Marquez (1995), Banco deEspana (2003) for comprehensive surveys of earlier studies and new results.

B. Algieri (B)Department of Economics and Statistics, University of Calabria, Ponte P. Bucci, 87036 Cosenza, Italye-mail: [email protected]

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not entirely explain export performances; factors such as non-price competitivenessmay also play a crucial role. This conclusion seems to be confirmed by the persistingresiduals resulting from the estimated export equations for some of the EU membersas reported by the European Central Bank (OP 30, 2005) and OECD (2000, 2005).This study therefore introduces, in addition to the conventional variables affectingexport volumes, an unobserved component (UC)—the non-price factor—in the formof time-varying trends to pick up stochastic unmodelled behaviours of the series andto avoid spurious regressions. Non-price factors include aspects such as advancedtechnology and the quality of a product. The technique adopted in this study enablesdetermination of the relative price elasticities in a more accurate way and featuringthe impact of the non-price competitiveness factor on export performance.

Persons (1919, 1926) was the first economist to clearly recognise the presence ofUCs into time series. He stated that time series are made of four types of fluctuations:a long time tendency or secular trend; a wavelike or cyclical movement superimposedupon the secular trend; a seasonal movement within the year; and a residual variationdue to developments in individual series. Afterwards, Harvey (1989, 1990) and Harveyand Shephard (1993) formally systematised a structural modelling approach to treattrends and seasons as UCs. Following these above authors, the UC modelling has beenadopted for the analysis of GDP, consumption, unemployment and inflation.2

In the field of international economics, the UC model has not been adopted yet. Infact, the empirical study on trade has been confined to the estimation of demand func-tions for exports that resemble the imperfect substitute model, i.e. regressing exportvolumes on the level of economic activity at home or abroad and on relative tradedgoods’ prices, with attention closely focused on the estimated income and price elastic-ities. The novelty of this study consists in applying for the first time the UC techniqueto a group of export equations to enhance the estimation of export volumes and toextract the maximum possible amount of information from the time series by isolatingthe regularities and ‘laws’ governing export fluctuations.

The analysis is conducted for the Euro Area (EA) and the five big EA countries,namely: France, Germany, Italy, the Netherlands and Spain and for the three majorcompetitors, i.e. the UK, the USA and Japan. The study covers the period from 1978:1to 2009:1. Quarterly data are used.

The plan of the article is as follows. Section 2 presents the background of the study.Section 3 outlines the theoretical framework to assess export equations and set outthe model. Section 4 provides the empirical evidence and displays the econometricfindings comparing alternative scenarios. The determinants of the stochastic trend areexplored in Sect. 5 and the conclusions are presented in Sect. 6.

2 Gerlach and Smets (1998) used the UC methodology for the estimation of the EMU area GDP. Orlandiand Pichelmann (2000) adopted a multivariate extension of a UC model to decompose the EUR-11 unem-ployment in its cyclical and trend elements. Sarantis and Stewart (2001) have shown how the potentialmisspecification of a consumption function can be ameliorated by approximating any long-run variationwith an UC in the form of a stochastic trend.

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Modelling export equations using an unobserved component model 595

2 Trade modelling

The behaviour of foreign trade flows has been subjected to many empirical inves-tigations through the estimation of trade equations. The latter are equations for thetime-series behaviour of the quantities and prices of imported and exported goods.3

The question of how the time-series behaviour of imports and exports should be mod-elled has been subject to much debate. The appropriate model relies on the type oftraded commodity, i.e. whether it is a homogeneous or a differentiated good; the mainpurpose to which the traded product is destined, i.e. whether it used as factor of pro-duction or as final good; the institutional and legal structure under which trade takesplace; the aim of the modelling analysis, i.e. whether it is necessary to forecast or totest hypotheses; and the availability of data, i.e. whether data are annual or quarterlyor whether they are disaggregated or aggregated. The empirical literature has beencharacterised by two general models of trade, namely, the imperfect substitutes modeland the perfect substitutes model. The two models have often been considered as com-petitors, because most trade analyses have gauged aggregate exports and/or imports.When aggregation is no more a severe constraint and it is possible to disaggregatedata, the two aforementioned models could be viewed as complements: ‘one concern-ing trade for differentiated commodities and the other regarding trade for close, ifnot perfect, substitutes’ (Goldstein and Khan 1985; Senhadji and Montenegro 1998).As regards elasticities’ values, the survey conducted by Goldstein and Khan (1985)shows that the long-run price elasticities of the estimated demand for imports andexports for industrial countries fall in a range from 0 to −4.0, whilst income elastici-ties fall between 0.17 and 4.5. Since the values of price elasticities vary considerably,the recent literature questions the effectiveness of real devaluation in affecting exportsand imports. According to Rose (1990, 1991) and Ostry and Rose (1992), a real depre-ciation does not impact significantly on the trade balance. Reinhart (1995), Senhadjiand Montenegro (1998, 1999) provide instead, strong support to the view that depre-ciations improve the trade balance. This study goes a step forward in the literature,because it sets up the trade equation in a structural dimension, allowing for a thirdvariable—the UC—to play a role in explaining export flows.4 Besides being valuablein understanding trade links between countries, export elasticities come about becausetheir role in the development of policies to deal with slowdowns is in relative demand.Cross-price elasticities also help the pricing and marketing strategies.

3 Early estimations of income and price elasticities have been investigated and assessed by Horner (1952)and Prais (1962). Early world trade models are examined in Taplin (1973). Multi-country models have beengauged by Deardorff and Stern (1978). Special attention should be paid to trade surveys by Leamer andStern (1970), Stern et al. (1976) and to the studies by Chipman (1985), Goldstein and Khan (1978, 1985),Herd (1987), Marquez and McNeilly (1988), Faini et al. (1992), Hung et al. (1993), de la Croix and Urbain(1995), Hooper et al. (1998), Senhadji and Montenegro (1999), Nielsen (2001), Banco de Espana (2003),Allard et al. (2005), Lissovolik (2008) and Bussière et al. (2009).4 Albeit the first idea of using a stochastic trend can be allotted to Anderton (1992), this article differs fromthe previous study as it is—to our knowledge—the first which systematically analyses the relative meritsof price and non-price competitiveness in explaining export volumes. Methodologically, we go beyond thepure empirical exercise adopted by Anderton which imposed a specific trend profile without testing forseveral trend specifications. Besides, we tried to explain some of the factors lying behind the trend itself.

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An examination of the existent empirical literature on export equations is summa-rized in Table A1 (Annex A). As one can notice, price elasticity varies across studiesand according to the estimation technique adopted.

3 Econometric methodology

3.1 The unobserved component model

There are two main approaches to UC model identification and estimation: the struc-tural form and the reduced form methodology. The first considers the UC model asthe observation equation of a discrete time stochastic State Space Model; the KalmanFilter is then adopted to estimate the time-variable parameters in the regression. Thesecond approach makes use of the Box-Jenkins procedure. This study applies the firstmethodology because, focusing ‘on the stochastic properties of the data’ (Harvey andJaeger 1993) endorses to capture changing behaviours of structural time series. In otherwords, the structural time-series approach offers a framework in which the concept ofequilibrium may be interpreted in a more dynamic perspective, since it can be analysedin the context of a slowing, changing environment. This more flexible method enablesus to capture the underlying competitiveness of the exports after controlling for theimpact of price competitiveness and foreign demand.

The basic structure of a UC model as developed by Harvey (1989) can be expressedas

yt = α + πxt + μt + εt (1)

where α is a constant, xt is a k × 1 vector of exogenous regressors, (πt is a k × 1vector of coefficients,μt is the time-varying trend or UC, εt is an irregular or transientcomponent normally distributed with zero mean and constant variance. The xt vectorincludes lagged values of the dependent variable as well as lagged values of exogenousvariables.

In general, the trend componentμt assumes the local linear specification as reportedbelow:

μt = μt−1 + βt−1 + ηt ηt ≈ NID(

0, σ 2η

)(2)

βt = βt−1 + ξt ξt ≈ NID(

0, σ 2ξ

)(3)

where Eq. 2 defines the level of the trend and Eq. 3 its slope (β), i.e. its growth rate.εt , ηt and ξt are normally distributed stochastic error terms independent from eachother in all time periods. The local linear trend specifies both the level and the slopeto be stochastic, but the trend can also be a random walk with drift (local level withdrift) when σ 2

ξ = 0 and σ 2η > 0 or random walk without drift (local level) when

σ 2ξ = 0, σ 2

η > 0 and β = 0. A trend has a smooth specification when the level is

fixed (σ 2η = 0) and the slope stochastic (σ 2

ξ > 0). The model collapses to a global or

deterministic trend when σ 2ξ = σ 2

η = 0. It is important to note that, to have a stationary

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disturbance term, it is necessary differencing twice in the case of local linear trend,and it is instead necessary to difference once in the case of local level with or withoutdrift and deterministic trend (Harvey and Scott 1994). In the latter case, Eq. 1 becomes

yt = α + πxt + δxt−1 + μt + εt (4)

where the lagged variables constitute the long-run dynamics and the differenced ones() represent the short-run dynamics.

3.2 The ECM specification

A generalised formulation of our error correction trade model incorporating an UCand allowing for relative export price fluctuation can be expressed as follows5:

lxvolt = ∂1l f dt + ∂2lr pxt − γ1lmst−1 + γ2lr pxt−1 + μt + εt (5)

μt = μt−1 + βt−1 + ηt ηt ≈ NID(

0, σ 2η

)(6)

βt = βt−1 + ξt ξt ≈ NID(

0, σ 2ξ

)(7)

where xvol is the country’s export volume, fd is foreign demand, rpx denotes relativeexport price,6 ms is the market share; is the difference operator, l is logarithm; μ,the latent component, mirrors different phenomena, namely the globalisation of pro-duction, the ongoing technological advances on the supply side, and the requests for

5 According to the ECM lxvolt = ∂1l f dt + ∂2lr pxt − λ(lxvolt−1 − α1l f dt−1 − α2lr pxt−1 −α3μt ) + εt which follows the expression formulated by Harvey (1989). Using the property of logarithmand defining λ = γ1;α1 = 1;α2λ = γ2, and α3 = 1/λ, we have lxvolt = ∂1l f dt + ∂2lr pxt −γ1(lxvolt−1/ l f dt−1)+ γ2lr pxt−1 + μt + εt . Equation 5 is obtained knowing that ms = xvol/ f d.6 There is a little consensus on what should be the ‘best indicator’ for price competitiveness. In general,two classes of indicators can be distinguished. The first includes a broad range of real effective exchangerates (based on CPI, PPI, GDP deflator, unit labour costs, etc.). The second comprises relative export prices.Boyd et al. (2001) review the use of relative export prices in the literature. Ca’ Zorzi and Schnatz (2007)have examined the properties of six measures of price competitiveness for the Euro Area, five based on thereal effective exchange rate and one on the relative export prices. They have found little evidence showingthat there is one indicator that outperforms the others in explaining and forecasting the Euro Area exports.However, comparing the out-of-sample forecasting performance of alternative cost and price competitive-ness measures, the two authors find that the relative export prices provide the most accurate forecasts ofexport volumes, if a recursive structure is used. Along these lines, di Mauro and Forster (2008) use relativeexport prices to measure price competitiveness, because such indicators are a better gauge of a country’scapacity to compete in export markets and a better predictor of export performances. The two authors infact explain that relative export price indicator includes firms’ pricing to market strategies, i.e. how firmsoffset exchange rate movements by adjusting their profit margin instead of instantly passing them on inthe prices charged to their foreign consumers. This is also explained in Chinn (2006). Similar to all otherindicators, relative export prices show a number of probable shortcomings too (for a complete discussionsee ECB 2003). For instance, it is usually more complex to obtain comparable export price measures amongdifferent countries than for other indicators of price and cost competitiveness. Meanwhile, some alternativecompetitiveness indicators have been developed, see Thomas et al. (2008) for the USA. However, thesemeasures are typically not available for all the countries. It was, therefore decided to use the relative exportprices in view of the data coverage, their extensive use in the empirical literature and their properties.

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increasing quality standards on the demand side. Non-price competitiveness can there-fore be distinguished into a technological non-price competitiveness factor, such aspatenting activity, R&D expenditures, FDI, product quality and a structural non-pricecompetitiveness factor; for instance, educational attainment and business environment.The latter may denote the legal and institutional framework (labour market regulations)of a country, its international image, its tax system and its production infrastructure(capital investment is necessary for the improvement of efficiency in production). ε, ηand ξ are normally distributed random disturbances with zero mean and constant var-iance, σ 2. As before, the lagged variables in levels represent the long-run equilibriumequation, whilst the differenced variables define the short-run equilibrium.

4 Econometric analysis

4.1 Data

Quarterly data for France, Germany, Italy, Netherlands, Spain, the UK, the USA andJapan have been collected from NiGEM database. Euro Area (EA)7 figures were takenfrom ECB. The sample ranges over the period from 1978:1 to 2009:1. The dependentvariable of Eq. 5, namely export volumes,8 has been based, 2000=100 (Annex B,Figs. A1, A2).

The chosen explanatory variables are export market share, price variables andnon-price variables. Their behaviours and development are reported in Annex B. Theexport market share (Annex B, Figs. A3, A4) is defined as a ratio of country’s exports toa weighted average of imports of its main destination markets. In other words, exportmarket share is the ratio of country’s export volumes (xvol) to its foreign demand(fd) (or export market index). The index is expressed in volume terms to make itdirectly react to changes in price competitiveness. If export market share were insteadexpressed in value terms, then the index would have been influenced by changes inexport volumes and changes in relative price. Export volumes refer to the exports ofgoods and services. Foreign demand9 is gauged as a weighted average of the importvolumes of main trading partners, with weights being defined as the share of eachdestination in total exports. The weighting system was drawn from NiGEM and theECB for the Euro Area.10 The relative export price is defined as the ratio of competi-

7 The Euro Area comprises 12 of the current 27 Member States of the European Union, namely Austria,Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal and Spain.8 Euro Area export volumes include intra- and extra Euro Area trade.9 Foreign demand can be defined either as a weighted average of output in foreign countries (Hooper et al.2000), or as a weighted average of foreign imports (e.g. Anderton et al. 2004). This latter definition wasused because the ratio of exports to foreign demand can be interpreted as market share.10 In the ECB data set, weights are based on trade in manufactured goods and services with the tradingpartners in specific periods (e.g. 1995–1997; 1998–2000; and 2001–2004) and are calculated to accountfor third market effects. Since overall trade patterns tend to change only gradually, the weights are updatedat three-year intervals. In Nigem database, weights are revised on yearly basis, according to a simpletime-trend growth between 1981 weights and 2003 weights. The advantage of this structure is that itenables us to catch the prevailing structure of the markets. Further details on the methodology underlyingthe weighting scheme can be found in Nigem Database, ECB monthly bulletin, September 2004, Box 10.

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tors’ export prices, expressed in US$, over export prices (domestic exporter’s prices).An increase in country i relative export price signals a gain in price competitivenessfor that country. Vice-versa, a drop in country i relative export price leads to a rise inthe other country’s competitiveness. Relative export price and foreign demand havebeen based, 1992 = 100 (Annex B, Figs. A5, A6). The time series exhibiting seasonalpatterns have been seasonally adjusted using the Census-X12 additive method.

Different non-price competitiveness variables have been used specifically: patents,FDI, gross fixed capital formation, R&D, HT, medium–high-tech, medium–low-techand low-tech (HT, MHT, MLT and LT, respectively) industries (Annex C, Figs. A7,A8, A9). These last four variables mirror the quality of products. Yearly patent datawere collected from the U.S. Patent and Trademark Office (USPTO); the figures referexactly to the number of patents granted as distributed by year of patent grant. Thepercentage quotas were calculated for each country. The relative direct investmentabroad (outflows) and from abroad (inflows) were constructed as the ratio of a coun-try’s FDI expressed in US billion dollars to the country’s nominal GDP expressedin US billion dollars. The gross fixed capital formation in domestic currency wascorrected by GDP in domestic currency. Data on foreign direct investment and grossfixed capital formation were extracted from the IMF, WEO data-stream. Technologyindicators have been collected from the OECD STAN database and are computed asratio between the shares of HT, MHT, MLT and LT industries in each country to thezone total exports. R&D is defined as gross domestic expenditure on R&D as % ofGDP; data were drawn directly from Eurostat, Technology and Science Indicators.

4.2 Variable analysis

First, the quantitative variables have been transformed in logarithms (l), namely thenatural logarithm of foreign demand, of relative export price and market share. Thelogarithmic form has the major advantage to express the resultant coefficients as elas-ticities of the variable included.

Second, the order of integration in each series has been tested using the adjustedDickey–Fuller (ADF) (Dickey and Fuller 1979) and the Phillips–Perron (PP) (Phillipsand Perron 1988) tests. The results for the individual time series are reported inTable A2 (Annex A). The critical values are those computed by McKinnon; the testsinclude both an intercept and a trend, since this specification is a plausible descriptionof the considered data. The proper lag length has been selected on the basis of theSchwarz–Bayesian Criterion, which chooses the appropriate lag length by trading offparsimony against reduction in the sum of squares. As the fit of the model increases,the SBC will approach to −∞. The PP test uses instead the Newey-West bandwidthto select the proper lag structure.

The export volumes, the relative export price and the export market share seriesfor each considered country are integrated of order one I(1), i.e. the series becomestationary after the first difference. A dissimilar result between the two tests was foundfor Japan regarding export volumes and export market share. Following the ADF test,in fact, the series are stationary at 5% level, whilst according the PP test, the seriesshow a unit root. A contrasting finding was observed for the Netherlands regarding

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export volumes and relative export prices. The export volume series is integrated oforder two following the ADF, whilst is integrated of order one according the PP test.According to the ADF test, the relative export price series is stationary at 5% criticalvalues; following instead the PP test, the series has a unit root. The PP results werefollowed because the test is considered more powerful than the ADF, as the formeruses consistent estimators of the variance. It can, therefore, be concluded that all theseries are random walk processes.

It should be noted, however, that, although testing for unit roots has become a man-datory step in applied economics, according to Harvey (1997) ‘this is, much of thetime, either unnecessary or misleading, or both’. (…) ‘Within the structural frameworkdeciding on the degree of integration is not crucial, because if the slope is determinis-tic, the parameter which allows it to change over time will be estimated as zero or closeto zero. The same is true of the level. Thus very little, if anything, is lost by startingoff with a general stochastic trend model which has the deterministic slope and levelas special cases. If there really is a need to test whether a component can be treatedas deterministic, then it is better to use a test which is not based on the autoregressiveapproximation’ (pp. 196–197).

4.3 Estimates of export equations with unobserved components

A number of specifications were estimated for export volumes. All estimations andtest statistics were produced with the econometric software STAMP 8 (Koopman et al.2007), which maximises a likelihood function using the Kalman Filter with diffuseinitial conditions. The specification of the components was carried out on the basis ofthe salient features of the series.In general, when the variables are I(1), the appropriatespecification of the model is a local level with-or-without drift, when variables are I(2)the specification should be a local linear level. However, Harvey (1997) has pointedout that, since unit root tests are virtually incapable of indicating that a process isintegrated of order two, other specifications should be investigated. Starting from theBasic Structural Model which specifies a stochastic level and stochastic slope, makingup the trend, a stochastic season11 and an irregular,12 the variances of disturbances inthe components were evaluated. When a parameter was found equal to zero, the corre-sponding component was set to fixed. The preliminary analysis of the UC is reportedin Annex A (Table A3). Since the slope, season and cycles of the series were estimatedto be null, trends were modelled as random walk with drift (Koopman et al. 2007).This is in fact reflected in the non-zero estimates of the trend level in the finale statevector. One may choose to exclude the drift either if it is not significant on the wholesample or if the explanatory power of the estimation decreases consistently. The latterwas our case, and thus the drift has been included. To explore the appropriateness of

11 Although the series has been seasonally adjusted, it is known that not all the adjustment proceduressucceed in removing seasonality, and thus the seasonal option was not de-activated to assess whether anyseason was significant. In addition, tests for residual seasonality were conducted, bearing in mind that ifthe residuals show serial correlation, then a season at that lag should be added.12 xvolt = μt + γt + ψt + εt where μt is the trend level, ψt is the stochastic cycle, γt is the stochasticseason, and εt is the irregular.

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the stochastic specification, the model was estimated into three different scenarios.The first is the most unrestricted one and allows for a stochastic trend according tothe state equations (2) and (3); the second scenario corresponds to the conventionalstatic model, with fixed dummies and deterministic linear time trend; finally, the thirdscenario is the simplest one, it shows no trends.

The three models have been estimated with and without the long-run unit foreigndemand elasticity constraint imposed (ms = xvol/ f d). This practice is in line withthe studies by Sarantis and Stewart (2001), OECD (2000, 2005), ECB (2005) and diMauro and Forster (2008). We only report the estimates with the imposed constraintas the results are more robust both in terms of diagnostic checking and significanceof coefficients.

The estimation report of Eqs. 1–4 and the diagnostic summary in the three differentsettings for the five EA countries, the EA in aggregate and the three major tradingcompetitors, are displayed in Tables A4, A5 and A6 (Annex A). Interventions, in theform of irregular and level components were introduced where residuals of exportvolumes exhibited an outlier. More clearly, irregular interventions are like dummyvariables; they correct for transitory, non-typical observations. Level interventionsaccommodate permanent step shift in the series; they resemble structural breaks. Theestimation reports (Annex A, Tables A4, A6, A7) give information about convergence(i.e. strong or weak convergence), the maximized value of the log-likelihood function,the prediction error variance (p.e.v.) and a set of summary statistics for the estimatedresiduals, such as a normality test, the Box–Ljung and Durbin-Watson tests for absenceof serial correlation and a test for the absence of heteroskedasticity.

More specifically, normality is tested according to the Doornik-Hansen correctionto the Bowman-Shenton statistic. The latter has a Chi-square distribution with twodegrees of freedom under the null hypothesis of normally distributed errors. The nullis rejected if the calculated probability exceeds the tabulated ones equal to 5.99 at5% significance level, and 9.21% at 1% significance level. The heteroskedasticity teststatistics (H(h)) is distributed as a F(h, h) with (h, h) degrees of freedom. Under thenull of no heteroskedasticity and for h = 33−36, the 5% critical value is 1.75; forh = 37−40, the 5% critical value is 1.84. The serial correlation coefficients (r) at thefirst and ninth lag are distributed as a N(0, 1/T ), T being the number of observations.ρ < 0.02 at 5%. The classical Durbin Watson test is distributed as N(2, 4/T ). TheLjung–Box statistics (Q(P, d)) is based on the sum of the first P autocorrelations,and it is tested against a Chi-square distribution with d degrees of freedom. The nullhypothesis of no autocorrelation is tested against the alternative of autocorrelation.The critical value for eight degrees of freedom is 15.51 at 5% significance level.

The baseline specifications13 for each setting have been chosen on the basis of theirrobustness, and thus on the minimization of the AIK, BIC and the error prediction.

In the first setting (Annex A, Table A4), the estimated coefficient displays theexpected signs. The diagnostic checking rejects the presence of serial correlation, het-eroskedasticity and non-normality. The estimates show good explanatory power forall countries as highlighted by the R2 values. The correct specification of the model

13 The model does not include seasons, because both the initial analysis and the residual test did not showany serial correlation at the seasonal lags (Koopman et al. 2007, p. 57).

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is testified by the low values of the prediction error variance, i.e. the variance of theone-step ahead prediction errors in the steady state, and by the ratio of the predic-tion error variance and the mean deviation in squares near to 1. Finally, the modelstrongly converged in few iterations, which are generally an indication of good results(Koopman et al. 2007). To test whether the residuals of the long-run equations areI(0), an ADF test has been carried out. Specifically, Table A5 (Annex A) shows thatthe residuals of the estimated export equations are stationary, and this confirms that along-run relationship exists for each country.

In the second setting (deterministic trend), there are some problems of sign regard-ing r px for France and the Netherlands; there is autocorrelation in the residuals forFrance as testified by the Ljung–Box statistics and the coefficients of the exogenousvariables are not significant for Italy (Annex A, Table A6).

In the third scenario, the simplest one, some autocorrelation is found for the Nether-lands and the UK. Italy and Japan show non-significant coefficients for the independentvariables. The third model specification is also the one that displays the lowest R2, asexpected (Annex A, Table A7).

On the basis of specification and misspecification tests and of goodness of fit crite-ria, the most appropriate specification is that of the first setting. This implies that thelong-run relationship obtained by Eqs. 5–7 is the following:

ln xvolt = ln f dt + γ2

γ1ln r pxt + 1

γ1μt+1

where the stochastic trend μt+1 is given by

μt+1 = μt + β + ηt+1

Table 1 reports the long-run export equations with the long-run unit foreign demandelasticity constraint imposed, given the estimates of the long-run coefficients and thehyper-parameters, σ 2

η and σ 2ξ .

The two explanatory variables in the final model are all statistically significant withprice response inelastic. The relative small effect of prices appears consistent with theestimates in the literature for Member States (Gagnon 2003; European Commission2004; OECD 2005, 2010; ECB 2005; ECFIN 2009). In general, according to theliterature, there are differing views about the size of long-run elasticities and, as aconsequence, on the effectiveness of the exchange rate in altering nominal trade bal-ance in the long run. These views can be distinguished in elasticity optimistic versuselasticity pessimists. In our case, the resulting low elasticity may arise from an aggre-gate estimation of export volumes which offset the different price elasticities withinmanufacturing and service sectors. Services are indeed less price sensitive than themanufactured trade. More in detail, price elasticity is in the range of 0.3–1.0 as pos-tulated by Halliwell and Padmore (1985). It is computed that a 10% increase in rpx,i.e. a gain in competitiveness, raises export volumes by about 4.8, 4.4, 4.1 and 3.6% inthe EA, the UK, the USA and Japan, respectively. As regards the European members,Spain registers the highest price elasticity, whilst the Netherlands has the lowest value.

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Table 1 Long-run exportvolumes relationship

ln logarithm, xvol exportvolumes, fd foreign demand, rpxrelative export prices

ln xvolt = ln f dt + γ2γ1

ln r pxt + 1γ1μt+1

France

ln xvolt = ln f dt + 0.45 ln r pxt + 1.59μt+1

μt+1 = μt + β + ηt+1

Germany


μt+1 = μt + β + ηt+1

Italy

ln xvolt = ln f dt + 0.43 ln r pxt − 1.16μt+1

μt+1 = μt − β + ηt+1

Netherlands


μt+1 = μt + β + ηt+1

Spain


μt+1 = μt + β + ηt+1

Euro Area


μt+1 = μt + β + ηt+1

UK


μt+1 = μt + β + ηt+1

USA


μt+1 = μt + β + ηt+1

Japan

ln xvolt = ln f dt + 0.36 ln r pxt − 1.92μt+1

μt+1 = μt − β + ηt+1

This means that negative changes in price have more harmful effects for Spain thanthe other EU countries.

It is interesting to notice that the price elasticity in setting 1 is generally lowerthan those obtained in settings 2 and 3 (Annex A, Table A8). This can be due to thestrongest role played by the UC—non-price factor—in the form of time-varying trendin the first specification. In addition, the error correction coefficients are much largershowing a more rapid adjustment towards equilibrium. In setting 1, the adjustmentcoefficients imply relative rapid adjustment of exports for Italy, France and the USA,and a more moderate speed of adjustment for the other countries.

To examine the stability of price elasticities, the export equations have beenre-estimated for three sub-samples of 10 years each. The first sub-period spans from1978:Q1 to 1987:Q4; the second covers the quarters 1988:Q1–1997:Q4, the thirdspans from 1998:Q1 to 2009:Q1. The results, reported in Table A9 (Annex A), show

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604 B. Algieri

that there is a broad evidence in favour of the stability of the parameters. We performalso a battery of Chow breakpoint tests (Annex A, Table A4) which conclude that theequations are stable.

4.4 Trend analysis

The trend is the long-run component in the series and indicates the general directionin which the series is moving. The extent to which trend component evolves overtime depends on the parameters σ 2

η and σ 2ξ which have been estimated by maximum

likelihood in the time domain. Table A4 (Annex A) reports the estimated standarddeviations and the signal-to-noise ratio of the residuals driving the UC.

The signal-to-noise ratio being different from zero, the permanent component isconfirmed to be stochastic. If the signal-to-noise ratio were equal to zero, then thetime-series model representing a decomposition into permanent plus transient com-ponents would have contained a deterministic trend. Moreover, since the proportionthat is not explained by the model, i.e. the transitory noise, is minimised, model 1 iswell specified. The contribution of the trend component to the annual rate of exportgrowth within sample is positive for all the countries with the exception of Italy andJapan. The trend term reduces export performance by 0.6 and 0.4% per year in Japanand Italy, respectively. A negative time trend indicates secular declines in trade shareas also confirmed by Figs. A3 and A4 (Annex B). The negative trend effect could beimputed to the sizeable outward FDI for Japan (Pain and Wakelin 1997; OECD 2005)and the low R&D investment in Italy (The Economist 2005). Amongst the economieswith an underlying trend improvement in export performance, the largest effect at theend of the year can be seen in the UK and Spain. These effects are likely to be related tothe positive impact of inward foreign direct investment on export performance (OECD2005; The Economist 2005).

The stochastic trends are all statistically significant14, and they have a long-run elas-ticity greater than one for all the considered countries, with the EA, the UK, the USAand the Netherlands having the largest values. This implies the presence of substantialunobserved long-run effects, which are effectively captured by the stochastic trend(Table 1). The behaviour of the stochastic trend is shown in Figs. 1 and 2. Figure A10(Annex C), extracting the permanent component from the equation, gives a clear pic-ture to what extent trends contribute to estimate the model. It appears that stochastictrends ameliorate the evaluation of the export volume equations.

5 Unobserved component specification

In order to explain the underlying performance of exports, the estimated stochastictrend has been modelled in a yearly panel equation using a fixed effect (FE) modelover the period 1980–2008. Explicitly, the dependent variable is the estimated stochas-

14 The statistical significance of the stochastic component is gauged with reference to the ratio of theestimated coefficient and its root mean squared error (t-ratio).

123


80

85

90

95

100

105

110

1978

-4

1980

-3

1982

-2

1984

-1

1985

-4

1987

-3

1989

-2

1991

-1

1992

-4

1994

-3

1996

-2

1998

-1

1999

-4

2001

-3

2003

-2

2005

-1

2006

-4

2008

-3

Trend_DLUKXVOL

Trend_DLUSXVOL

Trend_DLJPXVOL

Fig. 1 Stochastic trend of the main EU competitors. Base 1992 = 100. Source: Own elaborations

85

90

95

100

105

110

1978

-3

1979

-4

1981

-1

1982

-2

1983

-3

1984

-4

1986

-1

1987

-2

1988

-3

1989

-4

1991

-1

1992

-2

1993

-3

1994

-4

1996

-1

1997

-2

1998

-3

1999

-4

2001

-1

2002

-2

2003

-3

2004

-4

2006

-1

2007

-2

2008

-3

Trend_DLEUXVOL

Trend_DLITXVOL

Trend_DLFRXVOL

Trend_DLGEXVO

Trend_DLNLXVOL

Fig. 2 Stochastic trend of the main EU countries. Base 1992 = 100. Source: Own elaborations

tic trend15 (stochtrend) and the regressors are nine variables, proxies for non-pricecompetitiveness, namely: the percentage quotas of patents (M), gross fixed capitalformation (GFCF), R&D, FDI inflows and outflows (FDI1 and FDI2) and HT, low-,MHT, and MLT industries (HT, LT, MHT, MLT). The FE model below was specifiedand estimated.

15 Figures were transformed in yearly data using the moving average procedure.

123

606 B. Algieri

Table 2 Panel least squareDep. variable = TrendFixed effect estimates. Sample: 1980 2008White period standard errors and covariance (no d.f. correction)

Coefficient Std. error t-Statistic P[Z > z]Gross fixed capital formation 0.839287 0.367088 2.286338 0.0232

MHT industry 0.726354 0.358761 2.024616 0.0441

MLT industry 1.561790 0.366015 4.267007 0.0000

High-tech industry 1.238943 0.368684 3.360444 0.0009

R&D 1.155012 0.315247 3.663831 0.0004

LT industry 1.672140 0.446789 3.742574 0.0002

Patents −0.876796 0.580560 −1.510259 0.2971

FDI inflows 0.451900 0.261248 1.729776 0.0850

FDI outflows 0.480122 0.388515 1.235789 0.2178

C 148.3708 26.86973 5.521857 0.0000

R2 0.580254 AIK 6.274489

F-statistic 23.55540 DW 1.86

Prob(F-statistic) 0.000000 No. of observations 243

Log-likelihood −744.3504 Cross section incl 9

stoch trendi t = β0 + β1 · (FDI1)i t + β2 · (FDI2)i t + β3(GFCF)i t + β4(HT)i t+β5(LT)i t + β6(MHT)i t + β7(MLT)i t+β8(M)i t + β9(R&D)i t + εi t

εi t ≈ N (0, σ 2ε )

The ‘i’ indexes cross-sectional realizations so that i = 1. . .N and ‘t’ indexestime-series realizations so that t = 1. . .T . The individual effect beta zero is con-sidered to be constant over time and specific to the individual cross-sectional unit.Country’s fixed effects have been included to control for unmeasured country-specificfactors that may influence stochastic trends. To avoid errors correlation over time, wehave estimated the regression in levels with robust standard errors.16 Levels have theadvantage of a straightforward interpretation of the parameters (i.e. elasticities).

Results for fixed effect estimation are reported in Table 2. Seven out of the nineindependent variables were statistically significant, accounting for 58% of the vari-ance in the dependent variable. All the estimated coefficients enter the equation withpositive signs. This means that FDI inflows, high technology and investment have apositive effect on stochastic trend. The residual analysis (Fig. A11, Annex C) showsthat residuals are normal. The resulting estimates are heteroskedasticity consistent17

and unbiased.

16 To have standard errors robust to serial correlation (Arellano 1987; White 1980); the White period hasbeen chosen as the coefficient covariance method.17 The results use robust White period standard errors with no d.f. correction.

123


6 Conclusions

As the worldwide process of economic change and liberalisation continues, interna-tional competitiveness can be expected to change over time. In this article, the devel-opment of export performance and underlying competitiveness in the Euro Area, theUK, the USA and Japan have been examined. In the traditional models of trade, pricecompetitiveness and foreign demand are the main variables which influence exportperformance. This article introduces a non-price dimension of competitiveness in theframework of an UC approach to model trade equation. This methodology allows usto pick up underlying changes in export performance after controlling for the impactof price competitiveness and foreign demand. If the non-price information were infact overlooked, then the long-run export equations would be misspecified and spuri-ous regressions would have occurred. The adopted approach therefore overcomes anymisspecification by proxying any long-run variation with a latent component in theform of time-varying trend.

The model, estimated in three different settings, shows how significant are timestochastic trends in explaining the core performance of exports. Using a set ofdiagnostic checking, we fail to detect any misspecification. Besides, the signal-to-noise testifies that the model is well specified since the transitory noise is minimised.

The estimated stochastic trend elasticities are well above the unity, showing howthey effectively capture the changes that occurred during the years in the EA, the UK,the USA and Japan. The estimated price elasticities in the set of volume equations forgood and services are generally small (in the range 0.3–0.8%). This is consistent withother empirical studies and it testifies the strong role played by the UC in explainingexport volumes.

In the last part of the article, proxies for non-price competitiveness—such as R&Dor investment performance—have been regressed against the time stochastic trends toexplain part of the nature of the stochastic trend. The empirical evidences from theadopted fixed effect panel data model suggest that the stochastic trend is influenced byFDI inflows, high technology, R&D and investment. These variables have a positiveeffect on stochastic trend, the variable FDI outflows and patents turned to be not sig-nificant. Note that the best performing proxy for non-price competitiveness can thenbe used as an additional indicator for the outlook for exports.

Acknowledgments The author wishes to thank Professor Lucio Sarno, University of Warwick, ProfessorBob Anderton, University of Nottingham and ECB, Dr. Laurent Maurin ECB, Professor Antonio AquinoUnical, Ian Hurst National Institute of Economic and Social Research Global Econometric Model, twoanonymous referees and the Editor of this Journal for insightful suggestions and comments. The authorwould like to thank participants of seminars at the University of Calabria (2009) and at the Bonn University(2009) for their useful comments.

Annex A

123

608 B. Algieri

Tabl

eA

1C

ompa

riso

nof

estim

ated

long

-run

pric

eel

astic

ities

for

expo

rts

Inve

stig

ator

Exp

ort

pric

eel

astic

ityC

ount

ryE

stim

atio

npe

riod

Lev

elof

aggr

egat

ion

Type

ofeq

uatio

ns/m

odel

OE

CD

(201

0)−0

.60

USA

Exp

ortv

olum

esof

good

san

dse

rvic

esSt

anda

rdex

port

equa

tions

−0.5

1E

uro

Are

a(Q

uart

erly

data

)

−1.0

0Ja

pan

Ca’

Zor

zian

dSc

hnat

z(2

007)

0.61

Eur

oA

rea

(usi

ngre

l.ex

p.p.

)19

92:Q

1–20

06:Q

1E

xpor

tvol

umes

ofgo

ods

and

serv

ices

Stan

dard

expo

rteq

uatio

nsin

aE

CM

fram

ewor

k0.

3–0.

4E

uro

Are

a(u

sing

5ty

pes

ofre

x.)

diM

auro

and

Mau

rin

(200

5)0.

58E

uro

Are

a19

92–2

003

Exp

ortv

olum

esof

good

san

dse

rvic

esSt

anda

rdex

port

equa

tions

with

cons

trai

nton

fore

ign

dem

and

and

tren

dte

rm0.

54Fr

ance

0.42

Ger

man

y

0.42

Ital

y

0.35

Net

herl

ands

0.58

Spai

n

OE

CD

(200

5)−0

.604

Fran

ce19

82–2

002

(qua

rter

lyda

ta)

Exp

ortv

olum

esof

good

san

dse

rvic

esSt

anda

rdex

port

equa

tions

with

long

-run

elas

ticity

ofun

ityim

pose

don

expo

rtm

arke

tsi

zean

dtim

etr

end

ina

EC

Mfr

amew

ork

−0.4

66G

erm

any

−0.6

04It

aly

−0.6

04N

ethe

rlan

ds

−1.0

47Sp

ain

−0.6

04U

K

−0.6

04U

SA

123


Tabl

eA

1C

ontin

ued

Inve

stig

ator

Exp

ort

pric

eel

astic

ityC

ount

ryE

stim

atio

npe

riod

Lev

elof

aggr

egat

ion

Type

ofeq

uatio

ns/m

odel

−1.0

47Ja

pan

−1.5

Chi

na

Eur

opea

nC

entr

alB

ank

(200

4)−0

.26

Eur

oA

rea

1991

:Q1–

2003

:Q3

Ext

ra-a

rea

expo

rts

ofgo

ods

and

serv

ices

Stan

dard

expo

rteq

uatio

nsin

aE

CM

fram

ewor

k

Ban

code

Esp

ana

(200

3)−0

.41

Fran

ce19

75:Q

1–20

01:Q

1V

olum

eof

man

ufac

turi

ngex

port

sSt

anda

rdex

port

equa

tions

ina

EC

Mfr

amew

ork

−1.0

8G

erm

any

−0.4

2It

aly

−0.3

4N

ethe

rlan

ds

−1.2

6Sp

ain

OE

CD

(200

0)Sy

stem

esti

mat

ion

resu

lts

Man

ufac

turi

ngex

port

volu

mes

Syst

emes

timat

ion

and

sing

leeq

uatio

nap

proa

chin

alo

gari

thm

icdy

nam

icer

ror

corr

ectio

nfo

rmw

ithin

two

scen

ario

s:se

emin

gly

unre

late

dre

gres

sion

estim

atio

ns(S

UR

E)

and

OL

S−0

.99

Fran

ce19

75–1

997

(sem

i-an

nual

data

)

−0.9

9G

erm

any

−0.9

9It

aly

−0.5

4N

ethe

rlan

ds

−0.9

9Sp

ain

−0.9

9U

K

−0.6

3U

SA

−1.6

9Ja

pan

123

610 B. Algieri

Tabl

eA

1C

ontin

ued

Inve

stig

ator

Exp

ort

pric

eel

astic

ityC

ount

ryE

stim

atio

npe

riod

Lev

elof

aggr

egat

ion

Type

ofeq

uatio

ns/m

odel

Sing

leeq

uati

ones

tim

atio

ns−0

.81

(lin

ear

tren

d)Fr

ance

−0.6

0(n

on-l

inea

rtr

end)

−1.4

4(l

inea

rtr

end)

Ger

man

y

−1.0

5(n

on-l

inea

rtr

end)

−0.9

8(n

otr

end)

Ital

y

−0.3

9(n

otr

end)

Net

herl

ands

−1.4

1(l

inea

rtr

end)

Spai

n

−1.4

0(n

on-l

inea

rtr

end)

−1.5

8(n

otr

end)

UK

−0.5

6(n

otr

end)

USA

−1.4

1(l

inea

rtr

end)

−1.4

0(n

on-l

inea

rtr

end)

Japa

n

−2.1

5(n

otr

end)

−0.6

8(l

inea

rtr

end)

Chi

na

−0.4

8(n

on-l

inea

rtr

end)

EU

RO

MO

N(2

000)

−0.8

8Fr

ance

1970

–199

9(q

uart

erly

data

)E

xpor

tvol

umes

ofgo

ods

and

serv

ices

Stan

dard

expo

rteq

uatio

nsin

am

ulti-

coun

try

mod

el−0

.77

Ger

man

y

−0.5

6It

aly

−0.2

0N

ethe

rlan

ds

−0.2

0Sp

ain

123


Tabl

eA

1C

ontin

ued

Inve

stig

ator

Exp

ort

pric

eel

astic

ityC

ount

ryE

stim

atio

npe

riod

Lev

elof

aggr

egat

ion

Type

ofeq

uatio

ns/m

odel

Senh

adji

and

Mon

tene

gro

(199

9)

−0.0

2Fr

ance

1960

–199

3E

xpor

tof

good

san

dno

n-fa

ctor

serv

ices

Fully

mod

ify

estim

atio

n

−0.1

4It

aly

−1.2

7Ja

pan

−0.1

8Sp

ain

−0.3

5U

K

−0.7

3U

SA

−3.1

3C

hina

Cap

oral

ean

dC

hui(

1999

)−0

.08

(DO

LS)

;−0

.04

(AR

DL

)Fr

ance

1960

–199

2E

xpor

tvol

umes

ofgo

ods

and

serv

ices

Joha

nsen

Proc

edur

eus

ing

two

tech

niqu

es:

(1)

DO

LS

proc

edur

ede

velo

ped

bySt

ock

and

wat

son

(199

3);(

2)A

utor

egre

ssiv

edi

stri

bute

dla

g(A

RD

L)

−0.1

1(D

OL

S);

−0.1

0(A

RD

L)

Ger

man

y

−0.9

3(D

OL

S);

−0.4

7(A

RD

L)

Ital

y

−0.1

6(D

OL

S);

−2.4

2(A

RD

L)

Net

herl

ands

−1.9

3(D

OL

S);

−1.2

2(A

RD

L)

Spai

n

−0.1

9(D

OL

S);

−0.2

9(A

RD

L)

UK

−0.6

3(D

OL

S);

−1.3

6(A

RD

L)

USA

−1.7

0(D

OL

S);

−0.1

9(A

RD

L)

Japa

n

123

612 B. Algieri

Tabl

eA

1C

ontin

ued

Inve

stig

ator

Exp

ort

pric

eel

astic

ityC

ount

ryE

stim

atio

npe

riod

Lev

elof

aggr

egat

ion

Type

ofeq

uatio

ns/m

odel

Hoo

per

etal

.(19

98)

−0.2

Fran

ceV

aryi

ngpe

riod

sfr

om19

70s

to19

97fo

rFr

ance

,Ger

man

y,It

aly.

From

mid

1950

s/ea

rly

1960

sto

1994

Q4

or19

97Q

1fo

rth

eot

hers

Rea

lexp

orts

ofgo

ods

and

serv

ices

Coi

nteg

ratio

nve

ctor

san

dE

CM

−0.3

Ger

man

y

−0.9

Ital

y

−1.6

UK

−1.5

USA

−1.0

Japa

n

And

erto

n(1

991)

Trad

itio

nal

mod

el19

71:Q

2–19

88:Q

4M

anuf

actu

ring

expo

rtvo

lum

esTw

osc

enar

ios:

trad

ition

alm

odel

and

stoc

hast

ictr

end

mod

el−0

.32

Ital

y

−0.2

7G

erm

any

−0.4

7U

K

−0.6

5U

SA

−1.1

1Ja

pan

Stoc

hast

ictr

end

1971

:Q2–

1988

:Q4

−0.3

2U

K

−0.6

2U

SA

−0.8

5Ja

pan

Lan

desm

anan

dSn

ell

(198

9)−0

.69

the

UK

1972

:1;1

979:

3;19

81:4

–198

6:Q

2M

anuf

actu

ring

expo

rts

Stan

dard

expo

rtde

man

d

Sour

ce:O

wn

elab

orat

ions

123


Tabl

eA

2U

nitr

oott

ests A

ugm

ente

dD

icke

y–Fu

ller

test

stat

istic

aPh

illip

–Per

ron

test

stat

istic

b

AD

FL

evel

AD

Ffir

stdi

ffer

ence

PPL

evel

PPfir

stdi

ffer

ence

t-St

atis

ticPr

ob.*

t-St

atis

ticPr

ob.*

t-St

atis

ticPr

ob.*

t-St

atis

ticPr

ob.*

Exp

ortv

olum

es

lfr

−1.7

6155

60.

7175

−9.7

7106

90.

0000

lfr

−1.9

4854

10.

6230

−9.9

9404

80.

0000

lge

−2.0

1846

00.

5852

−5.8

1777

30.

0000

lge

−1.6

5995

50.

7630

−5.7

8223

80.

0000

lit−2

.348

772

0.40

44−1

2.65

627

0.00

00lit

−1.6

4508

00.

7693

−11.

4919

50.

0000

ljp−3

.959

232

0.01

25ljp

−2.3

9438

10.

3807

−10.

5455

90.

0000

lnl

−2.5

6318

10.

2979

−2.3

2278

70.

4182

lnl

−2.0

4304

80.

5718

−6.8

6118

30.

0000

lsp

−1.6

1947

00.

7798

−7.2

8603

50.

0000

lsp

−1.5

6842

80.

7999

−6.1

0305

30.

0000

luk

−2.3

6066

40.

3982

−9.8

6870

50.

0000

luk

−2.2

9819

60.

4315

−9.7

3745

20.

0000

lus

−3.2

5932

30.

0781

−3.8

8930

90.

0153

lus

−2.3

8588

20.

3851

−4.1

1931

10.

0077

lea

−2.6

9211

30.

2419

−7.9

8416

30.

0000

lea

−2.9

6036

00.

1481

−7.9

4809

70.

0000

Rel

ativ

eex

port

pric

e

lfr

−2.4

7146

40.

3417

−8.9

9005

30.

0000

lfr

−2.3

2895

50.

4150

−8.9

7119

90.

0000

lge

−2.1

3483

00.

5211

−7.6

2885

80.

0000

lge

−2.1

5063

50.

5123

−7.7

9723

70.

0000

lit−3

.015

427

0.13

23−8

.849

629

0.00

00lit

−2.7

0928

80.

2348

−8.7

8764

90.

0000

ljp−1

.558

560

0.80

35−6

.062

546

0.00

00ljp

−1.7

8005

50.

7086

−7.4

2409

70.

0000

lnl

−4.0

6089

80.

0092

lnl

−1.5

9384

80.

8434

−13.

4043

10.

0000

lsp

−1.6

6880

20.

7593

10.9

6804

0.00

00ls

p−2

.020

570

0.58

41−1

1.00

173

0.00

00

luk

−3.1

8358

80.

0926

−9.8

7253

60.

0000

luk

−2.8

0058

10.

2000

−9.8

2090

80.

0000

lus

−2.2

1195

70.

4784

−8.1

5168

90.

0000

lus

−2.1

9176

30.

4896

−8.2

6291

70.

0000

lea

−2.3

2373

60.

6928

−9.1

2637

90.

0000

lea

−2.2

5148

10.

4565

−9.0

3673

50.

0000

123

614 B. Algieri

Tabl

eA

2C

ontin

ued

Aug

men

ted

Dic

key–

Fulle

rte

stst

atis

tica

Phill

ip–P

erro

nte

stst

atis

ticb

AD

FL

evel

AD

Ffir

stdi

ffer

ence

PPL

evel

PPfir

stdi

ffer

ence

t-St

atis

ticPr

ob.*

t-St

atis

ticPr

ob.*

t-St

atis

ticPr

ob.*

t-St

atis

ticPr

ob.*

Mar

kets

hare

lfr

−0.4

4870

10.

9847

−12.

6991

40.

0000

lfr

−0.4

4870

10.

9847

−12.

6396

20.

0000

lge

−2.2

3418

60.

4663

−11.

5312

00.

0000

lge

−2.1

3052

00.

5235

−11.

6752

80.

0000

lit−0

.380

072

0.98

73−5

.510

912

0.00

01lit

−0.8

0186

20.

9620

−15.

3764

80.

0000

ljp−3

.572

203

0.03

65ljp

−2.1

2170

80.

5284

−8.9

0930

50.

0000

lnl

−2.5

8846

00.

2864

−14.

8876

20.

0000

lnl

−2.2

5984

40.

4522

−15.

6567

30.

0000

lsp

−0.6

4436

80.

9743

−12.

4476

00.

0000

lsp

−0.5

6836

20.

9789

−12.

3948

30.

0000

luk

−2.0

1412

90.

2805

−14.

4380

80.

0000

luk

−2.0

1412

90.

2805

−15.

1233

70.

0000

lus

−2.0

3777

70.

2705

−12.

0837

20.

0000

lus

−2.0

3564

20.

2714

−12.

0624

60.

0000

lea

−1.1

6187

90.

9129

−11.

5036

10.

0000

lea

−1.1

1346

70.

9217

−11.

5011

40.

0000

Nul

lHyp

othe

sis:

ther

eis

aun

itro

otM

acK

inno

n(1

996)

one-

side

dp-

valu

esa

Lag

Len

gth:

Aut

omat

icba

sed

Schw

arz

Info

rmat

ion

Cri

teri

onb

Lag

Len

gth:

Ban

dwid

thN

ewey

-Wes

tusi

ngB

artle

ttke

rnel

123


Table A3 Univariate structural time-series model for export volumes

Component DLFRXVOL (q-ratio)

Irr 0.0001319 (0.0071)

Lvl 0.018420 (1.0000)

Slp 0.00000 (0.0000)

Cy1 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLGEXVOL (q-ratio)

Irr 0.028626 (1.0000)

Lvl 0.00488 (0.1704)

Slp 0.00000 (0.0000)

Cy1 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLITXVOL (q-ratio)

Irr 0.031233 (1.0000)

Lvl 0.0193922 (0.6208)

Slp 0.00000 (0.0000)

Sea 0.00005 (0.0000)

Cy1 0.00000 (0.0000)

Component DLNLXVOL (q-ratio)

Irr 0.0085128 (0.5203)

Lvl 0.016362 (1.0000)

Slp 0.00000 (0.0000)

Cy1 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLSPXVOL (q-ratio)

Irr 0.030887 (0.6968)

Lvl 0.044321 (1.0000)

Slp 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLUKXVOL (q-ratio)

Irr 0.017652 (0.5617)

Lvl 0.031423 (1.0000)

Slp 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLUKXVOL (q-ratio)

Irr 0.014432 (0.8155)

Lvl 0.017698 (1.0000)

123

616 B. Algieri

Table A3 Continued

Component DLEUXVOL (q-ratio)

Slp 0.00000 (0.0000)

Cy1 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLUSXVOL (q-ratio)

Irr 0.015609 (0.4089)

Lvl 0.0381708 (1.0000)

Slp 0.00000 (0.0000)

Cy1 0.00000 (0.0000)

Sea 0.00000 (0.0000)

Component DLJPXVOL (q-ratio)

Irr 0.019927 (1.0000)

Lvl 0.014095 (0.7073)

Slp 0.00000 (0.0000)

Cy1 0.00000 (0.0000)

Sea 0.00000 (0.0000)

DLFRXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of distur-bancesDLGEXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of dis-turbancesDLITXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of distur-bancesDLNLXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of dis-turbancesDLSPXVOL = Trend + Dummy seasonal + Irregular. Estimated standard deviations of disturbancesDLEUXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of dis-turbancesDLUKXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of dis-turbancesDLUSXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations ofdisturbancesDLJPXVOL = Trend + 1 Cycle(s) + Dummy seasonal + Irregular. Estimated standard deviations of distur-bances

123


Tabl

eA

4E

xpor

tvol

ume

equa

tions

estim

ated

aw

itha

stoc

hast

ictr

end

and

non-

vary

ing

slop

e(l

ocal

leve

lwith

drif

t)

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

119

n=

123

n=

119

n=

114

n=

116

n=

110

n=

121

n=

119

n=

121

Fun

dam

enta

lpar

tb,c,

d

Lag

ged

expo

rtm

arke

tsha

re−0

.631

(0.1

2)−0

.57

(0.0

7)−0

.86

(0.0

9)−0

.51

(0.1

2)−0

.55

(0.0

8)−0

.27

(0.0

6)−0

.43

(0.1

1)−0

.51

(0.0

7)−0

.52

(0.0

9)

Lag

ged

rela

tive

expo

rtpr

ice

0.28

5(0

.07)

0.24

(0.0

4)0.

37(0

.13)

0.19

(0.0

6)0.

43(0

.07)

0.13

(0.0

3)0.

19(0

.06)

0.21

(0.0

5)0.

19(0

.05)

Dif

fere

nced

expo

rtm

arke

tsha

re−0

.18

(0.0

9)0.

17(0

.08)

0.14

(0.0

7)−0

.25

(0.0

7)0.

25(0

.06)

−0.2

1(0

.07)

−0.3

8(0

.10)

−0.1

7(0

.07)

−0.1

5(0

.07)

−0.1

7(0

.07)

0.18

(0.0

6)−0

.12

(0.0

6)−0

.14

(0.0

7)−0

.24

(0.0

7)−0

.30

(0.0

6)

−0.0

7(0

.05)

−0.1

5(0

.09)

Dif

fere

nced

rela

tive

expo

rtpr

ice

−0.2

1(0

.08)

0.12

(0.0

7)−0

.15

(0.0

5)

Tren

dde

com

posi

tion

,st

anda

rdde

viat

ions

ofdi

stur

banc

es(1

02)

σε

Irre

gula

r1.

042.

042.

110.

671.

440.

301.

030.

540.

84

ση

Tre

nd1.

150.

452.

121.

121.

161.

181.

421.

101.

49

qR

atio

0.90

0.22

0.99

0.60

0.81

0.21

0.73

0.50

0.56

Tren

dan

alys

isat

end

ofpe

riod

Lev

el4.

124.

705.

044.

011.

791.

762.

983.

644.

64

Gro

wth

rate

per

year

(%)

0.11

0.26

−0.3

80.

121.

260.

251.

300.

34−0

.62

Res

idua

lste

stse

Std

erro

r(1

02)

1.66

2.21

3.25

1.35

2.00

1.2

1.9

1.33

1.78

Nor

mal

ity0.

464.

120.

651.

790.

410.

380.

370.

420.

65

123

618 B. Algieri

Tabl

eA

4co

ntin

ued

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

119

n=

123

n=

119

n=

114

n=

116

n=

110

n=

121

n=

119

n=

121

H(h

)1.

44H

(38)

0.91

H(4

0)1.

16H

(39)

1.42

H(3

7)0.

64H

(38)

0.92

H(3

6)1.

58H

(39)

1.48

H(3

9)1.

23H

(39)

r(1)

(102)

−2.3

−1.6

−4.4

1.57

−0.4

−1.4

−1.7

8−2

.01

−4.3

4

r(9)

(102)

18.9

−4.6

2−2

.15

−4.3

4−0

.9−4

.5−7

.63

0.74

−0.

96

DW

1.99

1.98

2.04

1.86

1.97

1.67

2.01

2.00

2.07

Q(9

,8)

10.5

7.11

9.81

10.2

87.

3313

.00

10.0

45.

167.

13

Rdˆ

2(10

2)

69.5

670

.075

.671

.083

.669

.583

.47

68.2

770

.6

Goo

dnes

sof

fitre

sult

sfo

rre

sidu

alsf

Pred

ictio

ner

ror

vari

ance

(p.e

.v.)

0.00

0276

0.00

0482

0.00

1056

0.00

0184

0.00

0400

0.00

0153

0.00

0374

0.00

0176

0.00

0316

Rat

iop.

e.v.

/(pr

edic

tion

erro

rm

ean

devi

atio

n)2

1.10

1.15

1.07

1.05

1.21

1.07

1.04

1.10

1.20

AIC

−7.9

4−7

.47

−6.6

4−8

.45

−7.5

5−8

.53

−7.6

9−8

.41

−7.8

4

Con

verg

ence

Ver

yst

rong

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Cho

wbr

eakp

oint

test

g:

1988

:119

98:1

F-s

tatis

tic1.

591

[0.1

36]

1.95

1[0

.072

]0.

978

[0.4

57]

2.05

5[0

.062

]1.

418

[0.1

88]

0.88

0[0

.554

]1.

581

[0.3

87]

1.50

7[0

.163

]1.

472

[0.1

85]

123


Tabl

eA

4co

ntin

ued

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

119

n=

123

n=

119

n=

114

n=

116

n=

110

n=

121

n=

119

n=

121

Log

-lik

elih

ood

ratio

13.3

83[0

.099

]15

.397

[0.0

86]

8.39

7[0

.396

]15

.997

[0.0

80]

15.2

30[0

.124

]9.

737

[0.4

64]

13.2

76[0

.103

]12

.698

[0.1

23]

11.1

22[0

.133

]a

The

met

hod

ofes

timat

ion

ism

axim

umlo

g-lik

elih

ood.

The

Stat

eis

estim

ated

thro

ugh

aK

alm

anfil

ter

bT

hree

outli

ers

rela

tive

to19

81:4

,199

5:3

and

2007

:4w

ere

adde

dto

the

expo

rteq

uatio

nfo

rFr

ance

.Thr

eeou

tlier

sre

lativ

eto

1990

:3,1

991:

2an

d20

08:3

wer

ein

trod

uced

inth

eex

port

equa

tion

for

Ger

man

y.T

heir

resp

ectiv

eva

lues

are

0.07

,−0.

05an

d−0

.06

with

stan

dard

erro

rs0.

022,

0.02

3an

d0.

018.

Thr

eeou

tlier

sre

lativ

eto

1987

:3,1

988:

1an

d20

06:3

wer

ein

corp

orat

edin

the

expo

rteq

uatio

nfo

rIt

aly.

The

irre

spec

tive

valu

esar

e0.

08,−

0.09

and

−0.0

9w

ithst

anda

rder

rors

0.02

9,0.

029

and

0.02

8c

Four

outli

ers

rela

tive

to19

86:1

,198

8:3,

2002

:1an

d20

08:3

wer

ead

ded

toth

eex

port

equa

tion

for

the

Net

herl

ands

.The

irre

spec

tive

valu

esar

e0.

03,0

.06,

−0.0

4an

d−0

.03

with

stan

dard

erro

rsal

lequ

alto

0.01

.Fou

rou

tlier

sre

lativ

eto

1981

:3,1

982:

2,19

84:4

and

1986

:1w

ere

incl

uded

inth

eex

port

equa

tion

for

Spai

n.T

heir

resp

ectiv

eva

lues

are

0.06

,−0.

08,−

0.06

and

−0.0

8w

ithst

anda

rder

rors

0.06

,0.0

19,0

.018

and

0.01

8d

Four

outli

ers

rela

tive

to19

79:1

,197

9:2,

2006

:1,2

006:

3w

ere

inse

rted

inth

eex

port

equa

tion

for

the

UK

.The

irva

lues

are

−0.1

1,0.

17,0

.06,

0.17

,res

pect

ivel

y,an

dth

eir

stan

dard

erro

rar

e0.

021,

0.02

2,0.

022,

0.02

3.Tw

oou

tlier

sre

lativ

eto

1982

:1an

d20

08:3

wer

epu

tin

the

expo

rteq

uatio

nfo

rth

eU

SA.T

heir

valu

esar

e−0

.05

and

−0.0

8w

itha

stan

dard

erro

req

ualt

o0.

014

and

0.01

5.Fo

urou

tlier

sre

lativ

eto

1986

:1,1

988:

3,20

02:1

and

2008

:3w

ere

plac

edin

the

expo

rteq

uatio

nfo

rJa

pan.

The

irva

lues

are

−0.0

4,0.

05,0

.07

and

−0.1

5w

itha

stan

dard

erro

ral

lequ

alto

0.01

eN

orm

ality

iste

sted

acco

rdin

gto

the

Doo

rnik

-Han

sen

corr

ectio

nto

the

Bow

man

-She

nton

stat

istic

.The

latte

rhas

aC

hi-s

quar

edi

stri

butio

nw

ithtw

ode

gree

sof

free

dom

unde

rth

enu

llhy

poth

esis

ofno

rmal

lydi

stri

bute

der

rors

.We

reje

ctth

enu

llif

the

calc

ulat

edpr

obab

ility

exce

eds

the

tabu

late

don

eseq

ualt

o5.

99at

5%si

gnif

ican

cele

vela

nd9.

21%

at1%

sign

ific

ance

leve

l.H

(h)

isth

ehe

tero

sked

astic

ityte

stst

atis

tics

dist

ribu

ted

asa

F(h,

h)

with

(h,

h)

degr

ees

offr

eedo

m.U

nder

the

null

ofno

hete

rosk

edas

ticity

and

for

h=

33−3

6,th

e5%

criti

cal

valu

eis

1.75

.For

h=

37−4

0th

e5%

criti

cal

valu

eis

1.84

.r(1

)an

dr(

9)ar

eth

ese

rial

corr

elat

ion

coef

ficie

nts

atth

efir

stan

dni

nth

dist

ribu

ted

asa

N(0

;1/

T),

Tbe

ing

the

num

ber

ofob

serv

atio

ns.ρ

<0.

02at

5%.D

Wis

the

clas

sica

lDur

bin

Wat

son

test

dist

ribu

ted

asN

(2,4/

T).

Q(P,d

)is

the

Lju

ngB

oxst

atis

tics

base

don

the

sum

ofth

efir

stP

auto

corr

elat

ions

and

itis

test

edag

ains

taC

hi-s

quar

edi

stri

butio

nw

ithd

degr

ees

offr

eedo

m.T

henu

llhy

poth

esis

ofno

auto

corr

elat

ion

iste

sted

agai

nstt

heal

tern

ativ

eof

auto

corr

elat

ion.

The

criti

calv

alue

for

8de

gree

sof

free

dom

is15

.51

at5%

sign

ific

ance

leve

lf

The

pred

ictio

ner

ror

vari

ance

(p.e

.v)

isth

eva

rian

ceof

the

one-

step

ahea

dpr

edic

tion

erro

rsin

the

stea

dyst

ate.

Itgi

ves

am

easu

reof

the

prec

isio

nof

am

odel

’spr

edic

tions

.A

low

p.e.

v.(t

endi

ngto

zero

)m

eans

that

good

pred

ictio

nsar

eob

tain

edat

that

poin

t.A

ratio

p.e.

v./p

redi

ctio

ner

ror

mea

nde

viat

ion

insq

uare

sne

arto

1m

eans

that

the

mod

elis

corr

ectly

spec

ified

.AIC

isth

eA

kaik

eIn

form

atio

ncr

iteri

onus

edto

sele

ctth

epr

oper

mod

eles

timat

ion

gT

heC

how

brea

kpoi

ntte

stis

repo

rted

for

split

sin

1988

:1an

d19

98:1

.The

F-s

tatis

ticis

base

don

the

com

pari

son

ofth

ere

stri

cted

and

unre

stri

cted

sum

ofsq

uare

dre

sidu

als.

The

log-

likel

ihoo

dra

tiost

atis

ticis

base

don

the

com

pari

son

ofth

ere

stri

cted

and

unre

stri

cted

max

imum

ofth

e(G

auss

ian)

log-

likel

ihoo

dfu

nctio

n.T

heL

Rte

stst

atis

ticha

san

asym

ptot

icχ

2di

stri

butio

nw

ithde

gree

sof

free

dom

equa

lto(m

−1)

kun

der

the

null

hypo

thes

isof

nost

ruct

ural

chan

ge,w

here

mis

the

num

ber

ofsu

bsam

ples

and

kis

the

num

ber

ofpa

ram

eter

sin

the

equa

tion.

The

prob

abili

ties

are

give

nin

squa

red

brac

kets

123

620 B. Algieri

Table A5 Adjusted Dickey–Fuller test statistics of residuals

Export equation residuals t-Stat Prob.

France −8.461324 1% crit: −4.0437; 5% crit: −3.4508; 10% crit: −3.1505

Germany −5.929940 1% crit: −4.0393; 5% crit: −3.4487; 10% crit: −3.1493

Italy −8.562424 1% crit: −4.0422; 5% crit: −3.4501; 10% crit: −3.1501

Netherlands −6.782282 1% crit: −4.0406; 5% crit: −3.4519; 10% crit: −3.1512

Spain −6.509077 1% crit: −4.0429; 5% crit: −3.4504; 10% crit: −3.1503

Euro Area −4.136220 1% crit: −4.0494; 5% crit: −3.4535; 10% crit: −3.1521

UK −7.681970 1% crit: −4.0407; 5% crit: −3.4494; 10% crit: −3.1497

USA −6.925727 1% crit: −4.0437; 5% crit: −3.4508; 10% crit: −3.1505

Japan −6.118493 1% crit: −4.0407; 5% crit: −3.4494; 10% crit: −3.1497

123


Tabl

eA

6E

xpor

tvol

ume

equa

tions

estim

ated

with

ade

term

inis

tictr

end

(fixe

dle

vela

ndsl

ope)

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

123

n=

123

n=

119

n=

114

n=

116

n=

110

n=

121

n=

119

n=

121

Fun

dam

enta

lpar

t

Lag

ged

expo

rtm

arke

tsh

are

−0.0

68(0

.043

)−0

.368

(0.0

6)−0

.013

(0.0

3)−0

.027

(0.0

4)−0

.059

(0.0

3)−0

.211

(0.0

6)−0

.101

(0.0

5)−0

.334

(0.0

5)−0

.131

(0.0

4)

Lag

ged

rela

tive

expo

rtpr

ice

−0.0

16(0

.029

)0.

194

(0.0

3)0.

010

(0.0

7)−0

.033

(0.0

5)0.

044

(0.0

4)0.

148

(0.0

3)0.

168

(0.0

4)0.

175

(0.0

3)0.

068

(0.0

3)

Dif

fere

nced

expo

rtm

arke

tsha

re−0

.42

(0.1

1)0.

29(0

.09)

−0.2

5(0

.09)

0.31

(0.0

8)−0

.215

(0.0

8)−0

.417

(0.0

8)−0

.246

(0.0

7)−0

.18

(0.0

8)

0.21

(0.0

8)−0

.248

(0.0

7)

Dif

fere

nced

rela

tive

expo

rtpr

ice

0.13

(0.0

7)−0

.17

(0.0

6)

Tren

dde

com

posi

tion

,sta

ndar

dde

viat

ions

ofdi

stur

banc

es(1

02)

σε

Irre

gula

r1.

882.

290.

037

1.47

0.02

51.

570.

020

1.45

1.93

ση

Tre

nd

qR

atio

11

11

11

11

1

Tren

dan

alys

isat

end

ofpe

riod

Lev

el1.

432.

211.

021.

291.

021.

350.

741.

861.

08

Gro

wth

rate

per

year

(%)

0.02

0.16

0.03

0.10

0.14

3.06

0.25

−0.3

0

R.m

.s.e

.0.

210.

180.

290.

308

0.17

0.17

0.29

0.17

0.14

Res

idua

lste

sts

Std

erro

r(1

02)

1.8

2.2

3.5

1.4

2.3

1.5

1.9

1.4

1.8

Nor

mal

ity1.

180.

810.

272.

092.

6218

.31

4.00

0.34

3.21

H(h

)1.

37H

(40)

0.97

H(4

0)1.

08H

(39)

1.19

H(3

7)1.

66H

(38)

1.92

H(3

6)1.

92H

(39)

1.65

H(3

9)0.

78H

(39)

r(1)

(102)

14.6

−5.4

−4.6

2.73

−5.1

15.2

−9.4

18.2

4.66

r(9)

(102)

13.4

13.5

−3.1

1.34

−1.8

−3.2

−1.3

−4.9

−6.6

3

DW

1.69

2.00

1.97

1.41

1.84

1.41

1.77

1.56

1.89

123

622 B. Algieri

Tabl

eA

6co

ntin

ued

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

123

n=

123

n=

119

n=

114

n=

116

n=

110

n=

121

n=

119

n=

121

Q(1

0,10

)21

.57

15.1

13.4

22.6

714

.65.

1814

.212

.412

.5

Rdˆ

2(10

2)

62.2

868

.470

.769

.277

.754

.083

.365

.568

.5G

oodn

ess

offit

resu

lts

for

resi

dual

s

Pred

ictio

ner

ror

vari

ance

(p.e

.v.)

0.00

0326

0.00

0498

0.00

1268

0.00

196

0.00

0544

0.00

231

0.00

0375

0.00

0191

0.00

0339

Rat

iop.

e.v.

/(pr

edic

tion

erro

rm

ean

devi

atio

n)2

1.03

1.09

1.08

1.10

1.15

1.21

1.07

1.06

1.13

AIC

−7.8

6−7

.47

−6.5

2−8

.35

−7.2

9−8

.23

−7.7

0−8

.36

−7.7

9

123


Tabl

eA

7E

xpor

tvol

ume

equa

tions

estim

ated

with

outt

rend

and

slop

e

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

123

n=

123

n=

117

n=

108

n=

116

n=

110

n=

121

n=

119

n=

120

Fun

dam

enta

lpar

t

Lag

ged

expo

rtm

arke

tsha

re−0

.042

(0.0

2)−0

.051

(0.0

3)−0

.000

6(0

.03)

−0.0

43(0

.02)

−0.0

13(0

.01)

−0.0

44(0

.02)

−0.0

63(0

.03)

−0.0

33(0

.02)

−0.0

09(0

.01)

Lag

ged

rela

tive

expo

rtpr

ice

0.03

8(0

.02)

0.05

2(0

.03)

0.00

13(0

.03)

−0.0

40(0

.02)

0.01

7(0

.01)

0.04

7(0

.02)

0.06

5(0

.03)

0.03

6(0

.02)

0.01

1(0

.01)

Dif

fere

nced

expo

rtm

arke

tsha

re

−0.2

3(0

.10)

−0.2

3(0

.10)

0.27

2(0

.09)

−0.2

5(0

.08)

−0.1

7(0

.07)

−0.1

7(0

.08)

−0.2

6(0

.08)

−0.0

7(0

.03)

0.28

(0.0

7)

−0.2

1(0

.10)

−0.0

54(0

.09)

0.27

(0.0

8)−0

.08

(0.0

7)

Dif

fere

nced

rela

tive

expo

rtpr

ice

0.36

(0.0

7)−0

.18

(0.0

9)0.

402

(0.1

5)−0

.16

(0.0

6)0.

21(0

.08)

−0.1

4(0

.05)

0.21

(0.0

7)−0

.272

(0.1

5)0.

12(0

.06)

0.23

(0.0

8)

Stan

dard

devi

atio

ns(1

02)

ofdi

stur

banc

es

σε

Irre

gula

r1.

612.

603.

581.

352.

041.

472.

021.

641.

83

qR

atio

11

11

11

11

1

Res

idua

lste

sts

Std

erro

r(1

02)

1.52

2.49

3.43

1.29

1.89

1.43

1.95

1.57

1.73

Nor

mal

ity1.

075.

01.

461.

161.

081.

062.

341.

151.

73

H(h

)0.

75H

(39)

1.18

H(3

8)0.

91H

(39)

1.92

H(3

7)0.

72H

(38)

0.61

H(3

6)1.

73H

(40)

0.92

H(3

9)0.

8H

(40)

r(1)

(102)

−3.7

213

.52

−15.

9323

.4−1

0.09

21.1

11.5

17.6

−4.5

8

r(9)

(102)

18.2

3.65

−4.7

0−1

7.9

−6.3

−5.9

−2.2

1.2

−7.2

0

123

624 B. Algieri

Tabl

eA

7co

ntin

ued

1978

:1–2

009:

1Fr

ance

,G

erm

any,

Ital

y,N

ethe

rlan

ds,

Spai

n,E

uro

Are

a,U

K,

USA

,Ja

pan,

n=

123

n=

123

n=

117

n=

108

n=

116

n=

110

n=

121

n=

119

n=

120

Goo

dnes

sof

fitre

sult

sfo

rre

sidu

als

DW

2.06

1.60

2.26

1.49

2.18

1.57

1.71

1.64

2.08

Q(9

,9)

14.7

14.3

14.7

19.5

14.7

12.7

16.3

9.39

8.92

Rdˆ

2(10

2)

51.2

28.9

31.0

40.7

61.8

20.2

60.1

42.0

48.9

Pred

ictio

ner

ror

vari

ance

(p.e

.v.)

0.00

0232

0.00

0625

0.00

1175

0.00

0167

0.00

0359

0.00

0205

0.00

0379

0.00

0247

0.00

302

Rat

iop.

e.v.

/(pr

e-di

ctio

ner

ror

mea

nde

viat

ion)

1.16

1.20

1.18

1.11

1.19

1.01

1.09

1.12

1.17

AIC

−8.1

6−7

.22

−6.5

7−8

.50

−7.6

6−8

.38

−7.7

3−8

.14

−7.9

0

123


Table A8 Long-run elasticity of price competitiveness, comparative settings

France Germany Italy Netherlands Spain Euro Area UK USA Japan

Stochastic trend 0.45 0.42 0.43 0.37 0.78 0.45 0.44 0.41 0.36

Deterministic trend −0.24 0.53 0.77 −1.22 0.75 0.70 1.66 0.52 0.52

No trend 0.91 1.01 2.16 0.93 1.30 1.07 1.03 1.09 1.22

Table A9 Sub-sample priceelasticities

***/**/* Denotes statisticalsignificance at 99/95/90% level,respectively

1978:1–1987:4 1988:1–1997:4 1998:1–2009:1

FR 0.44** 0.49** 0.51**

IT 0.41** 0.38** 0.47***

GE 0.40** 0.45** 0.43**

NL 0.36** 0.40** 0.37**

SP 0.81** 0.77* 0.85**

EA 0.46** 0.44*** 0.51**

UK 0.44*** 0.41** 0.47**

USA 0.43** 0.40** 0.49***

JP 0.32** 0.37* 0.34*

Annex B: Variable developments

Export volumes

Starting with the general evolution from an aggregate perspective, export volumesfor the Euro Area (EA), the UK, the USA and Japan expanded between 1978 and2005. Towards the second half of 2008, all countries experienced a sharp contractionin export volumes as a consequence of the global financial crisis. Japan is the countrywith the highest export volumes until 1998, and from 2003 onwards. The United Statesshows the lowest export volumes excluding the years 1993–2000.

Within the EA, France and the Netherlands have registered the most regular grow-ing pattern of export volumes over time. Conversely, Italy, Spain and Germany haveshown more complex developments.

Export market share

To better assess the competitive position of a given country, it is necessary to analysethe evolution of its export market share. This is defined as a ratio of country’s exportsto a weighted average of imports of its main destination markets. Put it differently,export market share is the ratio of country’s export volumes to its foreign demand.The index is expressed in volume terms to make it directly react to changes in pricecompetitiveness. If export market share were instead expressed in value terms, thenthe index would have been influenced by changes in export volumes and changes inrelative price.

123

626 B. Algieri

20

40

60

80

100

120

140

160

180

1978

-1

1979

-3

1981

-1

1982

-3

1984

-1

1985

-3

1987

-1

1988

-3

1990

-1

1991

-3

1993

-1

1994

-3

1996

-1

1997

-3

1999

-1

2000

-3

2002

-1

2003

-3

2005

-1

2006

-3

2008

-1

JPXVOLUKXVOLUSXVOLEAXVOL

Fig. A1 Evolution of export volumes, base 2000 = 100. Source: Own calculations on NiGEM and ECBdatabases. Note: (1) Last observation refers to 2009Q1. (2) XVOL export volumes, JP Japan, UK UnitedKingdom, US United States, EA Euro Area

20

40

60

80

100

120

140

160

180

1978

-119

79-2

1980

-319

81-4

1983

-119

84-2

1985

-319

86-4

1988

-119

89-2

1990

-319

91-4

1993

-119

94-2

1995

-319

96-4

1998

-119

99-2

2000

-320

01-4

2003

-120

04-2

2005

-320

06-4

2008

-1

FRXVOL

GEXVOL

ITXVOL

NLXVOL

SP XVOL

Fig. A2 Evolution of export volumes, base 2000 = 100. Source: Own calculations on NiGEM and ECBdatabases. Note: (1) Last observation refers to 2009Q1. (2) XVOL export volumes, FR France, GE Germany,IT Italy, NL Netherlands, SP Spain

Figure A3 reports the evolution of export market share based 1992 = 100 for theEA as a whole vis-à-vis its major trading partners. Figure A4 sketches its evolutiondistinguishing by countries within the EA. Among the five major economies of theEA, market share in Spain expanded at a sustained rate during the first half of the 80sand from 90s onward.

Italy experienced a certain stability in export market share between 1973 and 1993,an increase until 1995 resulting from exchange rate depreciations and a substantialdecline from the second half of 1995 onwards. Germany increased its world exportmarket share until the reunification, after a substantial decline followed to the neweconomic and political context, it experienced an upsurge in its world market share,although the values are still below those recorded during the first half of the 1980s.

123


65

75

85

95

105

115

125

135

1978

-1

1979

-3

1981

-1

1982

-3

1984

-1

1985

-3

1987

-1

1988

-3

1990

-1

1991

-3

1993

-1

1994

-3

1996

-1

1997

-3

1999

-1

2000

-3

2002

-1

2003

-3

2005

-1

2006

-3

2008

-1

J P MS

UKMS

USMS

EUMS

Fig. A3 Export market share, 1992 = 100. Source: Own calculations on NiGEM and ECB databases.Note: (1) Last observation refers to 2009Q1. (2) MS export market share, JP Japan, UK United Kingdom,US United States, EA Euro Area

60

70

80

90

100

110

120

130

140

150

1978

-1

1979

-3

1981

-1

1982

-3

1984

-1

1985

-3

1987

-1

1988

-3

1990

-1

1991

-3

1993

-1

1994

-3

1996

-1

1997

-3

1999

-1

2000

-3

2002

-1

2003

-3

2005

-1

2006

-3

2008

-1

FRMS

GEMSITMS

NLMSSPMS

EUMS

Fig. A4 Export market share, 1992 = 100. Source: Own calculations on NiGEM and ECB databases. Note:(1) Last observation refers to 2005Q2. (2) MS export market share, FR France, GE Germany, IT Italy, NLNetherlands, SP Spain

The Netherlands registered a quite stable market share over the considered period,evidencing weak responses to the euro depreciations and appreciations. Since 2002,France has been experiencing a severe deterioration in export market share whosecauses have been subjected to a vast debate (Kierzenkowski 2009; Kabundi and NadalDe Simone 2009; Fontagnè and Gaulier 2008).

Relative export price and foreign demand

Price competitiveness, commonly defined as the capacity to compete at current pricesin international markets, is a key factor that shapes the evolution in exports and export

123

628 B. Algieri

Japan

60

80

100

120

140

16019

78-1

1980

-2

1982

-3

1984

-4

1987

-1

1989

-2

1991

-3

1993

-4

1996

-1

1998

-2

2000

-3

2002

-4

2005

-1

2007

-2

JPMS

JPRPX

UK

70

80

90

100

110

120

130

1978

-1

1980

-2

1982

-3

1984

-4

1987

-1

1989

-2

1991

-3

1993

-4

1996

-1

1998

-2

2000

-3

2002

-4

2005

-1

2007

-2

UKMS

UKRPX

US

707580859095

100105110

1978

-1

1980

-2

1982

-3

1984

-4

1987

-1

1989

-2

1991

-3

1993

-4

1996

-1

1998

-2

2000

-3

2002

-4

2005

-1

2007

-2

USMS

USRPX

Euro Area

80859095

100105110115120

1978

-1

1980

-2

1982

-3

1984

-4

1987

-1

1989

-2

1991

-3

1993

-4

1996

-1

1998

-2

2000

-3

2002

-4

2005

-1

2007

-2

EAMS

EARPX

Fig. A5 Export market share and relative export price comparison. Source: Own calculations on NiGEMand ECB databases. Note: (1) Last observation refers to 2009Q1. (2) MS export market share, RPX relativeexport prices, JP Japan, UK United Kingdom, US United States, EA Euro Area

40

90

140

190

240

290

1978

-1

1979

-3

1981

-1

1982

-3

1984

-1

1985

-3

1987

-1

1988

-3

1990

-1

1991

-3

1993

-1

1994

-3

1996

-1

1997

-3

1999

-1

2000

-3

2002

-1

2003

-3

2005

-1

2006

-3

2008

-1

FRSGESITSNLSSPSUKSUSSJPS

Fig. A6 Foreign demand, base 1992 = 100. Source: own calculations on NiGEM and ECB databases

market share. The variable is typically gauged in terms of relative export prices,although alternative definitions of real effective exchange rates can be adopted. Therelative export price is constructed as ratio of a weighted sum of competitors’ exportprice to domestic export price; therefore an increase in relative export price should beaccompanied by a rise in export market share. Figure A5 depicts the parallel evolution

123


of relative export price and export market share over time for the EA and the three bigcompetitors and for each of the major EA countries.

In general, there is a positive relationship between the two time series, more markedfor the EA and the USA, less evident for the UK and quite contradictory for Japan.Overall, it is interesting noting that the relation market share—relative export price isnot stable over time. Some structural factors can influence market share independentlyfrom the behaviour of prices. In the case of Japan, for instance, the outsourcing activi-ties may explain why in the last few decades the country experienced a drop in marketshare despite its gains in competitiveness. In the case of the USA, it has been argued(ECB: OP 30, 2005, Chap. 2) that the unresponsiveness of export markets share toprice movement could stem from the product composition of world demand and theexport production specialisation. In any case, J effects should be taken into account toexplain delayed response of export market share to changes in price competitiveness.

Foreign demand is estimated as a weighted average of the import volumes of majortrading partners, with the weights being equal to the share of each destination in totalexports.

Annex C

Product classification by technological intensity

High-technology industries (HT)Aircraft and spacecraftPharmaceuticalsOffice, accounting and computing machineryElectronics and communications equipmentMedical, precision and optical instrumentsMedium-high-technology industries (MHT)Electrical machinery and apparatus, n.e.s.Motor vehicles, trailers and semi-trailers, railroad and transportequipment, n.e.s.Chemicals excluding pharmaceuticalsMachinery and equipment, n.e.s.Medium-low-technology industries (MLT)Building and repairing of ships and boatsRubber and plastics productsOther non-metallic mineral products (including mining and quarrying)Basic metals and fabricated metal products (including mining and quarrying)Low-technology industries (LT)Wood, pulp, paper, paper products, printing and publishingAgriculture, fishing and food products, beverages and tobaccoTextiles, textile products, leather and footwearManufacturing of furniture, toys, not elsewhere specified products (n.e.s.)

Sources: OECD Science, Technology and Industry Scoreboard (2005, pp 181–183)

123

630 B. Algieri

0

10

20

30

40

80 85 90 95 00 05

EA_HT FR_HT GE_HTIT_HT JP_HT NL_HTSP_HT UK_HT US_HT

25

30

35

40

45

50

55

60

80 85 90 95 00 05

EA_MHT FR_MHT GE_MHTIT_MHT JP_MHT NL_MHTSP_MHT UK_MHT US_MHT

5

10

15

20

25

30

35

40

80 85 90 95 00 05

EA_MLT FR_MLT GE_MLTIT_MLT JP_MLT NL_MLTSP_MLT UK_MLT US_MLT

0

10

20

30

40

80 85 90 95 00 05

EA_LT FR_LT GE_LTIT_LT JP_LT NL_LTSP_LT UK_LT US_LT

Fig. A7 HT, LT, MHT, MLT industries. _HT HT industries. The ratio of the share in each country to thezone total exports. _LT LT industries. The ratio of share in each country to the zone total exports. _MHTMHT industries. The ratio of share in each country to the zone total exports. _MLT MLT industries. Theratio of share in each country to the zone total exports

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0

1

2

3

4

80 85 90 95 00 05

EA_R&D FR_R&D GE_R&DIT_R&D JP_R&D NL_R&DSP_R&D UK_R&D US_R&D

16

20

24

28

32

36

80 85 90 95 00 05

EA_GFCF FR_GFCF GE_GFCFIT_GFCF JP_GFCF NL_GFCFSP_GFCF UK_GFCF US_GFCF

-4

0

4

8

12

16

20

80 85 90 95 00 05

EA_FDI_IN FR_FDI_IN GE_FDI_INIT_FDI_IN JP_FDI_IN NL_FDI_INSP_FDI_IN UK_FDI_IN US_FDI_IN

0

5

10

15

20

25

80 85 90 95 00 05

EA_FDI_OUT FR_FDI_OUTGE_FDI_OUT IT_FDI_OUTJP_FDI_OUT NL_FDI_OUTSP_FDI_OUT UK_FDI_OUTUS_FDI_OUT

Fig. A8 Foreign direct investment inflows, outflows, gross fixed capital formation, R&D. R&D grossdomestic expenditure on R&D as % of GDP, GFCF gross fixed capital formation, current prices, nationalcurrency billion divided by nominal GDP national currency, FDI_IN direct investment abroad, inflows,US Billion Dollars, divided by nominal GDP$, FDI_OUT direct investment abroad, outflows, US BillionDollars, divided by nominal $GDP

123

632 B. Algieri

0

10

20

30

40

50

60

70

80 85 90 95 00 05

EA_PT FR_PT GE_PTIT_PT JP_PT NL_PTSP_PT UK_PT US_PT

Fig. A9 Patent quotas. _PT percentage quotas of patents

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96100

104

108

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116

120

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Fig. A10 Stochastic trends, market share Euro Area and big3 (100 = 1992)

123

634 B. Algieri

RoW

(10

0=19

92)

8084889296100

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80

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100

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60708090

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Fig. A10 continued

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0

5

10

15

20

25

30

-15 -10 -5 0 5 10

Standardized ResidualsSample 1980 2008Observations 243

Mean 1.39e-15Median 0.254308Maximum 13.93881Minimum -15.40892Std. Dev. 5.187716Skewness -0.116437Kurtosis 3.456540

Jarque-Bera 2.659423Probability 0.264554

Fig. A11 Residual analysis

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