CBCS WORKING PAPER

MEASURING CORE INFLATION IN CURAÇAO

MAY 2016

BY SHEKINAH DARE

CBCS.WP/16/2

2

Measuring Core Inflation in Curaçao

Simon Bolivar Plein 1Willemstad,

CuraçaoPhone: (599 9) 434-5500

Fax: (599 9) 461-5004E-mail: info@centralbank.cw

Centrale Bank van Curaçao en Sint MaartenResearch Department

Shekinah Dare

May 2016

CBCS.WP/16/1

Computing core inflation, that part of total inflation expected to persist beyond the short-run, is a crucial step in the design and implementation of a price stability program as done by the U.S. Federal Reserve and De Nederlandsche Bank. However, this is not the only reason to compute core inflation indicators. In small open economies, such as Curaçao, where inflation is deter-mined largely by external factors, measuring core inflation is key to better forecasting of near to medium-term overall inflation. This paper constructs eleven core inflation indicators for Curaçao based on the exemption clause, and assesses their effectiveness in not being biased, tracking trend inflation, and helping to predict (future) headline inflation. The main conclusion is that two core inflation indicators, the CPI excluding the highest persistence components and the CPI excluding chosen components, appear superior.

JEL Classification Numbers: E31

Keywords: Core inflation, inflation.

Author’s e-mail address: s.dare@centralbank.cw

This working paper expresses the views of the author, which do not necessarily represent those of the Centrale Bank van Curaçao en Sint Maarten. I would like to express my sincere appreciation

to Candice Henriquez, Fredericus Matto, Marelva de Windt, Natalia Koster, and Leonie Linderhof for their interest, support, and valuable contribution to this paper.

ABSTRACT

4

CONTENT

1 Introduction 2 The concept of core inflation3 Properties of core inflation4 Measuring core inflation5 Data and method6 Core inflation for Curaçao7 ConclusionReferencesAppendix

679

101215172123

5

As part of their price stability programs, most central banks, including the U.S. Federal Re-serve and De Nederlandsche Bank, compute core inflation to capture the underlying trend in inflation. Core inflation measures typically exclude some volatile components from the overall price index, for example, excluding food and energy prices from the CPI index because unusual price changes in these com-ponents are not likely associated with the un-derlying trend in inflation.

In small open economies like Curaçao, infla-tion is determined mostly by external fac-tors. The purpose of measuring core infla-tion in such economies is to capture the price changes that persist for years to forecast near to medium-term inflation (Blinder, 1997, and Bryan and Cecchetti, 1994). In Curaçao, un-usual price changes such as the increase in the sales tax rate from 5% to 6% in 2012 or the increase in gasoline prices in July 2011, March 2012, and September 2012, would be removed from the overall price index.

This paper contributes to academic literature by constructing a core inflation measure for Curaçao. The paper is organized as follows. The second section compares the headline or overall inflation1 with the core inflation. The third section discusses several ideal proper-ties that have been suggested for core infla-tion measures. The fourth section elaborates

1 The headline inflation refers to the CPI inflation.

on eleven core inflation measures developed for Curaçao. The fifth section outlines the data and method used to compute the core inflation indicators.2 The sixth section evalu-ates the core inflation measures based on their ability to not be biased, to track move-ments in trend inflation, and to predict future headline inflation. The seventh section con-cludes by revealing the preferable core infla-tion indicators for Curaçao.

2 The terms core inflation measure and core inflation indicator are used interchangeably in the paper.

INTRODUCTION1

6

Inflation reflects an increase in the average price of all goods and services in an econ-omy. As consumer prices increase, consum-ers’ purchasing power decreases, reflecting a drop in the value of money. Although there is no specific way to measure inflation, in most countries, including Curaçao, the Consumer Price Index (CPI) is used (Allen, 2000). CPI reflects the price change of a fixed basket of goods and services consumed by an average household in a particular period compared to a base period, i.e., the Laspeyres Weighted Formula:

where Pni is the current price of CPI component i, Poi is

the base price of CPI component i, Wi is the weight of

CPI component i, and m is the number of components

in the fixed basket.

In Curaçao, the CPI is calculated by the Cen-tral Bureau of Statistics (CBS) on a monthly basis. Inflation is determined as a change in the CPI, referred to as the headline, overall, or CPI inflation. The CBS publishes monthly inflation rates and annualized inflation rates (i.e., the 12-month average).

Despite its popularity, CPI inflation has often been criticized because it does not take into account the following biases:3

3 The biases are based on Neves and Sarmento, 1997.

1. Substitution bias: consumers substitute the more expensive goods and services for those that are relatively cheaper.

2. Quality change bias: improvement or worsening of the quality of certain goods and services.

3. New items bias: the introduction of new goods and services into the market.

4. New outlets bias: consumers prefer to buy goods and services at outlets with a better quality and price relationship.

Evidence shows that by not considering these biases, CPI inflation has been overestimating changes in the cost of living. Consequently, various statisticians have sought alternative ways to map the inflationary process (Allen, 2000).

Among these alternatives, core inflation has gained much attention in academic literature as it represents the long-run trend in the av-erage price of all goods and services in an economy. In the short-run, headline inflation can result from (1) temporary supply shocks, translated into sales price changes, (2) sea-sonal fluctuations, for example, holiday packages, and (3) other nonmonetary fac-tors, including indirect taxes, which do not have a long-lasting price effect. Rather than focusing on headline inflation, policy makers should focus on core inflation as it represents a filtered version of the former (Chamberlin, 2009, and Huwiler, 2009). Appendix 1 pro-vides a comparison between headline and core inflation.

THE CONCEPT OF CORE INFLATION2

7

Measuring core inflation constitutes a vital step in the design and implementation of a price stability program such as those con-ducted by the U.S. Federal Reserve and De Nederlandsche Bank. However, this is not the only reason to measure core inflation. In small open economies, such as Curaçao, in which inflation is determined largely by external factors, measuring core inflation is crucial to better forecasting of future CPI in-flation because core inflation is the part of ac-tual inflation expected to persist beyond the short-run.

8

Academic literature (Roger, 1998, Wynne, 1999, Marques et al., 2003, and Wiesiolek and Kosior, 2009) suggests that core infla-tion measures should exhibit certain desir-able properties:

1. Credible: A core inflation measure should be credible in the sense that the public is able to verify the calculations.

2. Simple: A core inflation measure should not be complex and, hence, be easily un-derstood by the public. As a result, de-viations from the headline inflation can be easily communicated to the public.

3. Robust: A core inflation measure should be robust by distinguishing persistent changes from temporary changes in headline inflation.

4. Not biased: The long-run average rate of a core inflation measure should be simi-lar to that of the headline inflation.

5. Timely: A core inflation measure should be measurable in a timely manner along-side headline inflation.

6. Reliable: A core inflation measure should not undergo considerable revisions un-less the underlying data change.

7. Predictive power: A core inflation mea-sure should be able to predict future headline inflation trends.

8. Track record: The properties and perfor-mance of a core inflation measure should be examined and evaluated to monitor their track record with respect to head-line inflation.

9. Economic rationale: A core inflation measure should be based on economic theory.

PROPERTIES OF CORE INFLATION3

9

No general consensus exists on the best technique to compute core inflation (Martel, 2008). However, two approaches can be dis-tinguished: (i) the statistical approach, and (ii) the model-based approach (Mankikar & Paisley, 2004).

The statistical approach performs several operations on the headline inflation index (Mankikar & Paisley, 2004). One of the fol-lowing techniques can be used to calculate core inflation:

1. Remove some components from the overall price index permanently or on a periodic basis, i.e., the exemption clause.

2. Reweight the price components with weights based on the volatility of prices, persistency of price changes, or a dy-namic factor model.

3. Apply a statistical method to deduce the long-term part of inflation (e.g., with a trend estimation or filter).

In contrast, the model-based approach applies multivariate econometric analysis supported by economic theory. The main advantage of this approach is that it explicitly considers headline inflation determinants. Nonetheless, for a number of reasons, central banks often apply the statistical approach instead of the model-based one to compute core inflation (Johnson, 1999).

First, when using the model-based approach, statisticians cannot always measure core in-

flation in a timely manner as several chal-lenges may arise from the structural models, notably Structural Vector Autoregression (SVAR) models, in terms of the model speci-fications, identification schemes, and restric-tions used. Second, the structural models ap-plied in the model-based approach are based on abstract concepts that are difficult for the general public to understand (Wiesiolek & Kosior, 2009).

Hence, the statistical approach is used here, and eleven core inflation measures are con-structed for Curaçao.

THE EXEMPTION CLAUSEIn computing core inflation, the most widely used technique in the statistical approach is the exemption clause because it is eas-ily computed and understood by the public (Wiesiolek & Kosior, 2009). The exemption clause is a popular method used in the United States and the Netherlands. According to the exemption clause, the overall CPI index is reweighted by placing zero weights on the most volatile components and rescaling the remaining ones (Allen, 2000, and Biccal et al., 2012). Appendix 2 describes the advan-tages and disadvantages of the exemption clause.

Most of the time, food and energy compo-nents are removed from the overall CPI index because these are subject to large, temporary price changes related to supply shocks (Al-len, 2000, and Clark, 2001). Gordon (1975)

MEASURING CORE INFLATION4

10

introduced the concept of core inflation as the overall price index excluding food and energy prices. However, though food and energy prices are normally considered the most volatile components, certain food and energy components are less volatile than oth-ers, while at the same time some non-food and non-energy components are highly vola-tile as indicated by Clark (2001). This paper, therefore, develops several variants of the exemption clause for Curaçao.

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The data cover 38 CPI components of Cura-çao as classified by the CBS, from September 1992 to July 2015. Even though Curaçao’s price index data are available since October 1990, the time period of the study starts in September 1992 because annualized monthly inflation rates are used.4 The total sample is divided into three groups because the weights of the CPI components were adjusted by the CBS in October 1990, February 1996, and October 2006. Group 1 covers from Sep-tember 1992 to January 1996 based on the weights of the CPI components in October 1990. Group 2 covers from February 1996 to September 2006 based on the weights of the CPI components in February 1996, and Group 3 covers from October 2006 to July 2015 based on the weights of the CPI com-ponents in October 2006 (see Appendix 3).

THE PRESENCE OF SEASONALITYFor each price component per group, the presence of seasonality is assessed through the combined seasonality test (ONS, 2007). This test specifies whether seasonality is identifiable by generating one of the follow-ing results:

i. Identifiable Seasonality Present, ii. Identifiable Seasonality Probably Not

Present, and

4 Annualized monthly inflation rates are calculated by dividing the 12-month average price index ending in a particular month by the 12-month average price index ending in that same month in the previous year.

iii. Identifiable Seasonality Not Present.

In the first two cases, it is recommended that price series be adjusted seasonally. If appli-cable, the series are seasonally adjusted with the X-12-ARIMA procedure provided by the U.S. Census Bureau (see Appendix 3). The aim of seasonally adjusting price series is to remove movements that occur with about the same timing and intensity each year. The sea-sonal component can result from (a) natural factors, e.g., weather-related conditions, (b) administrative measures, e.g., starting and ending dates of the school year, (c) social, cultural, & religious traditions, e.g., Christ-mas, and (d) the duration of months or quar-ters.

THE ADJUSTED CPI INFLATIONFigure 1 on page 18 compares the official CPI inflation published by the CBS with the adjusted CPI inflation, which is corrected for the updated relative importance weights. The adjusted CPI inflation indicates roughly what the official CPI inflation would have been if the weights of the CPI components were ad-justed more frequently than every 6-10 years. The figure shows that in some periods, the official CPI inflation is (almost) equal to the adjusted CPI inflation, while in other periods they differ considerably. The difference be-tween the official CPI inflation and the ad-justed CPI inflation ranges from a minimum of -0.6 to a maximum of 0.9. The adjusted CPI inflation is used to measure the core in-flation indicators in this paper (see Appendix

DATA AND METHOD5

12

4 for further details on how to measure rela-tive importance weights).

VOLATILITY AND PERSISTENCEVolatility is estimated by the standard de-viation (SD) of annualized monthly inflation rates per CPI component. Most of the CPI components with high volatility are related to food and energy prices (see Figure 2 on page 18). This result supports the choice of sev-eral central banks, including the U.S. Federal Reserve and De Nederlandsche Bank, to ex-clude these components from headline infla-tion when computing core inflation.

Persistence is the time it takes for a CPI component to return to its equilibrium after a shock, which is estimated by the sum of autoregressive coefficients. A higher sum of autoregressive coefficients means a higher persistence, but a lower persistence rank.

Cutler (2001) and Demarco (2004) used a first order Autoregressive model, AR(1) process, to estimate persistence, while the European Central Bank (2008) applied the Information Criteria technique to select the number of orders. To avoid subjectivity, this paper follows the Information Criteria ap-proach, meaning that the number of orders can vary per CPI component.

For each component, the following regres-sion is run:

where qi is the number of orders chosen for the AR

process to minimize the Schwarz Information Crite-

ria (SIC). As monthly CPI data are used, the number

of orders tested ranges from 1 to 12. In addition, ∆pit

stands for the annualized monthly inflation rate of CPI

component i at time t.

The autoregressive coefficients are then summed up:

CORE INFLATION INDICATORSIn this paper, eleven core inflation indica-tors are developed for Curaçao. Five of them are based on traditional methods applied in previous research. Another five of them are based on information provided by Clark (2001). The last core inflation indicator is based on the author’s assumptions.

The first core inflation indicator, i.e., the CPI excluding food & energy, is the adjusted CPI inflation excluding all food components and energy expenses. The second core inflation indicator, i.e., the CPI excluding fuel, is the adjusted CPI inflation excluding the expens-es for own transport vehicles (fuel prices). 5The third core inflation indicator, i.e., the CPI excluding food, is the adjusted CPI in-flation excluding all food components. The fourth core inflation indicator, i.e., the CPI excluding energy, is the adjusted CPI infla-tion excluding energy expenses.6 The fifth core inflation indicator, i.e., the CPI exclud-ing food & fuel, is the adjusted CPI inflation excluding all food components and expenses for own transport vehicles.

The sixth core inflation indicator, i.e., the CPI excluding 9 SD components, is the ad-justed CPI inflation excluding the nine com-ponents with the highest volatility, consist-ing of cereal products, meat & fish, fats & cooking oils, dairy products (except butter), tobacco, energy expenses, water, expenses for own transport vehicles, and hobby ar-

5 Fuel prices refer to the prices of gasoline.6 Energy expenses refer to the prices of electricity.

13

ticles. Similar to Clark’s selection criteria, each of these components is at least 3 times as volatile as the overall CPI inflation. As the number of excluded components is a matter of judgment and some of the most volatile components, such as tobacco and meat & fish are relatively persistent (see Figure 2 on page 18), the seventh core inflation indica-tor is constructed for comparison purposes. This core inflation indicator, i.e., the CPI excluding 4 SD components, is the adjusted CPI inflation excluding the four components with the highest volatility--fats & cooking oils, dairy products (except butter), energy expenses, and water.

The eighth core inflation indicator, i.e., the CPI excluding 11 persistence components, is the adjusted CPI inflation excluding the eleven components with the lowest persis-tence rank: clothing, dwelling costs, energy expenses, household articles, domestic ser-vices, expenses for own transport vehicles, communication, recreation, entertainment & culture, books, education, and hobby ar-ticles. CPI components with a persistence lower than 1 are excluded. The ninth core inflation indicator, i.e., the CPI excluding 5 persistence components, is the adjusted CPI inflation excluding the five components with the lowest persistence rank for comparison purposes: energy expenses, expenses for own transport vehicles, communication, en-tertainment & culture, and education. The tenth core inflation indicator, i.e., the CPI ex-cluding the highest persistence components, is the adjusted CPI inflation excluding the five components with the highest persistence rank: meat & fish, fats & cooking oils, dairy products (except butter), other food, and maintenance of dwelling.

The eleventh core inflation indicator, i.e., the CPI excluding chosen components, is the

adjusted CPI inflation excluding the fifteen components with a weight lower than 100: fats & cooking oils, dairy products (except butter), sugar & chocolate, prepared food, other food, tobacco, footwear, garden mainte-nance, upholstery & dwelling textile, house-hold appliances & tools, household articles, other household expenses, entertainment & culture, books, and hobby articles. This core inflation indicator is based on the assumption that components with the lowest weights are less important than other components in an average household’s basket of goods. Conse-quently, these components are less useful in predicting the overall CPI inflation.

Despite being founded on solid principles, all core inflation measures have their advan-tages and disadvantages. Therefore, as noted by Clark (2001),7 the fact that one measure is chosen over the other can be judged only by its empirical performance.

7 More core inflation indicators were developed by us-ing the volatility and persistence methods, but only the ones that performed better are presented in this paper.

14

The core inflation measures of Curaçao are assessed on their ability to not be biased, to track trend inflation, and to forecast future headline inflation.

NOT BIASEDFirst, a core inflation measure should not be biased, meaning that it should not consistent-ly diverge from the trend in headline infla-tion. This property can be assessed by com-paring the average rate of core inflation with that of the headline inflation for a long time period, i.e., 15 to 30 years, to see if the rates diverge in a statistically significant way. The following hypotheses are tested:

where and are the annualized monthly core

and headline inflation rates, respectively.

By this criterion, the CPI excluding fuel, the CPI excluding 5 persistence components, and the CPI excluding chosen components regis-ter the same average inflation rate of 2.6% as the headline inflation. In addition, the CPI excluding energy and the CPI excluding the highest persistence components record an average inflation rate of 2.5%, almost equal to that of the headline inflation (see Table 1 on page 19 for further details).

TRACK TREND INFLATIONSecond, a core inflation measure should track closely the trend rate of headline inflation;

when trend inflation increases, core infla-tion should rise commensurately and vice versa. Several methods are available to cal-culate trend inflation, but for core inflation, the most widely used method is the centered moving average (CMA) of headline inflation endorsed by Bryan and Cecchetti (1994). In this paper, trend inflation is estimated with a standard 3-month centered moving aver-age (CMA) and a Holt-Winters exponential smoothing (HWES) for comparison purpos-es.

To assess how closely core inflation tracks trend inflation, two methods are used. The first method measures the volatility around the trend, which is the standard deviation (SD) of the difference between core and trend inflation. If a core inflation measure tracks trend inflation closely, the differences tend to be small and, hence, the standard de-viation low. The second method is the root mean square error (RMSE):

For the RMSE, the core inflation measure with the lowest value tends to be the best.

Using the two criteria for tracking trend in-flation, i.e., the volatility around the trend and the RSME, the CPI excluding fuel, the CPI excluding energy, the CPI excluding the highest persistence components, and the CPI

CORE INFLATION FOR CURAÇAO6

15

excluding chosen components seem to per-form well (see Table 2 on page 20 for further details).

FORECAST FUTURE INFLATIONThird, a core inflation measure should help forecast future headline inflation. The pre-dictive ability of a core inflation measure can be judged with the Granger causality. If the Granger causality result of a core inflation indicator is significant, the core inflation in-dicator might be causing headline inflation. However, Granger causality can be applied only on stationary variables, i.e., variables that do not exhibit a unit root or despite the presence of a unit root, are cointegrated with the overall inflation. To check for the pres-ence of a unit root, the Augmented Dickey Fuller test is applied. The null-hypothesis is that there is a unit root. If the null hypothesis holds and, hence, the variable is nonstation-ary, the Engle-Granger test is conducted to check for cointegration with the overall in-flation.

Table 3 on page 21 shows that both the level and first differences of all core inflation mea-sures appear to be stationary because the null hypothesis is rejected. Hence, the core infla-tion measures should not be adjusted. Table 4 on page 22 shows that the Granger causal-ity test results are mixed. The CPI excluding the highest persistence components seems to be superior, showing significant Granger causality results for all forecast periods. The CPI excluding food, the CPI excluding food & fuel, and the CPI excluding chosen com-ponents also appear to have some predictive power, except for the shortest forecast period of 1 month and/or the longest forecast period

of 2 years.8

8 The inability of core inflation indicators to predict the headline inflation in the shortest period could be at-tributed to unanticipated price fluctuations in the short-term. In addition, the inability of core inflation mea-sures to forecast headline inflation in the longest period could be ascribed to insufficient information regarding price fluctuations in the future.

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CONCLUSION7

This paper assesses the effectiveness of elev-en core inflation measures developed for Cu-raçao by using three standard criteria pointed out in core inflation literature, i.e., the ability of a core inflation indicator to (1) not be bi-ased, (2) track trend inflation, and (3) help predict (future) headline inflation.

Regarding the first criterion, the CPI exclud-ing fuel, the CPI excluding 5 persistence components, the CPI excluding chosen com-ponents, the CPI excluding energy, and the CPI excluding the highest persistence com-ponents record (almost) the same average inflation rate of 2.6% as headline inflation. Regarding the second criterion, the CPI ex-cluding fuel, the CPI excluding energy, the CPI excluding the highest persistence com-ponents, and the CPI excluding chosen com-ponents seem to perform well. Regarding the last criterion, the CPI excluding the highest persistence components, the CPI excluding food, the CPI excluding food & fuel, and the CPI excluding chosen components seem to have (some) significant predictive power for headline inflation.

Overall, the results suggest that two core in-flation indicators can be considered superior: (1) the CPI excluding the highest persistence components, and (2) the CPI excluding cho-sen components.

Note, however, that a core inflation indica-tor can be constructed in several ways and that the matter of separating core from non-

core parts requires a great deal of judgment. Looking ahead, other core inflation indicators should be constructed for Curaçao to assess whether the chosen core inflation indicators in this paper remain preferable. If the statis-tical approach is used, it would be interest-ing to develop a core inflation indicator with the trimmed mean method, Kalman filter, or a dynamic factor model. If the model-based approach is used instead, it would be inter-esting to develop a core inflation indicator with a SVAR model.

17

-1

0

1

2

3

4

5

6

7

8

Adjusted CPI inflation Official CPI inflation

Source: Author’s calculations based on information from the Central Bureau of Statistics. Note that the official CPI inflation is published by the CBS, while the adjusted CPI inflation is the official CPI inflation corrected for the updated relative importance weights.

Figure 1: Comparison between the official CPI inflation and the adjusted CPI inflation

Source: Author’s calculations based on information from the Central Bureau of Statistics.

Figure 2: Volatility and persistence of CPI components

1.5

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18

Inflation measure AverAge rAtes (%)

Adjusted CPI inflation 2.6

CPI excluding food & energy 2.0

CPI excluding fuel 2.6

CPI excluding food 2.1

CPI excluding energy 2.5

CPI excluding food & fuel 2.0

CPI excluding 9 SD components 2.1

CPI excluding 4 SD components 2.4

CPI excluding 11 persistence components 3.1

CPI excluding 5 persistence components 2.6

CPI excluding the highest persistence components 2.5

CPI excluding chosen components 2.6

Table 1: Average rates for the adjusted CPI inflation and core inflation measures for the period September 1992 to July 2015

Source: Author’s calculations based on information from the Central Bureau of Statistics.

19

Infla

tion

mea

sure

vo

lAti

lity

Aro

un

d tr

end

(%)

rM

se (%

)

3-m

onth

CM

AH

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mon

th C

MA

HW

ES

Adj

uste

d C

PI in

flatio

n 0.

040.

440.

040.

44

CPI e

xclu

ding

food

& e

nerg

y 0.

680.

730.

950.

91

CPI

exc

ludi

ng fu

el

0.31

0.56

0.32

0.55

CPI e

xclu

ding

food

0.

510.

580.

720.

71

CPI

exc

ludi

ng e

nerg

y 0.

340.

560.

350.

55

CPI e

xclu

ding

food

& fu

el0.

740.

790.

990.

97

CPI e

xclu

ding

9 S

D c

ompo

nent

s1.

091.

181.

201.

24

CPI e

xclu

ding

4 S

D c

ompo

nent

s0.

650.

800.

690.

80

CPI e

xclu

ding

11

pers

isten

ce c

ompo

nent

s0.

570.

730.

710.

88

CPI e

xclu

ding

5 p

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tenc

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mpo

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s0.

550.

730.

550.

73

CPI

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0.33

0.50

0.37

0.49

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n co

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s0.

260.

440.

260.

44

Tabl

e 2:

Vol

atili

ties a

roun

d tr

end

and

RM

SE fo

r se

vera

l infl

atio

n m

easu

res f

or th

e pe

riod

Sep

tem

ber

1992

to J

uly

2015

Sour

ce: A

utho

r’s c

alcu

latio

ns b

ased

on

info

rmat

ion

from

the

Cen

tral

Bur

eau

of S

tatis

tics.

20

Ad

just

ed C

Pi

infl

Atio

n

CPi e

xCl

ud

ing

fo

od

& en

erg

y

CPi e

xCl

ud

ing

fu

el

CPi e

xCl

ud

ing

fo

od

CPi e

xCl

ud

ing

en

erg

y

CPi e

xCl

ud

ing

fo

od

& fu

el

Infla

tion

(leve

l)

AD

F-sta

tistic

-5.9

91-6

.310

-5.1

26-6

.240

-5.5

00-5

.666

p-va

lue

0.00

0***

0.00

0***

0.00

0***

0.00

0***

0.00

0***

0.00

0***

Cha

nge

in in

flatio

n (fi

rst d

iffer

ence

)

AD

F-sta

tistic

-5.2

44-5

.524

-4.2

16-4

.449

-5.0

94-5

.087

p-va

lue

0.00

0***

0.00

0***

0.00

5***

0.00

2***

0.00

0***

0.00

0***

CPi e

xCl

ud

ing

9

sd C

oM

Pon

ents

CPi e

xCl

ud

ing

4

sd C

oM

Pon

ents

CPi e

xCl

ud

ing

11

Pers

iste

nCe

Co

MPo

nen

ts

CPi e

xCl

ud

ing

5

Pers

iste

nCe

Co

MPo

nen

ts

CPi e

xCl

ud

ing

th

e hig

hes

t Pe

rsis

ten

Ce

CoM

Pon

ents

CPit

exCl

ud

ing

Ch

ose

n

CoM

Pon

ents

Infla

tion

(leve

l)

AD

F-sta

tistic

-3.8

69-4

.303

-5.2

95-5

.000

-5.7

92-5

.613

p-va

lue

0.01

5**

0.00

4***

0.00

0***

0.00

0***

0.00

0***

0.00

0***

Cha

nge

in in

flatio

n (fi

rst d

iffer

ence

)

AD

F-sta

tistic

-4.5

91-4

.572

-4.9

48-4

.125

-5.4

70-5

.345

p-va

lue

0.00

1***

0.00

1***

0.00

0***

0.00

7***

0.00

0***

0.00

0***

Tabl

e 3:

Uni

t roo

t tes

t res

ults

for

the

peri

od S

epte

mbe

r 19

92 to

Jul

y 20

15

Sour

ce: A

utho

r’s c

alcu

latio

ns b

ased

on

info

rmat

ion

from

the

Cen

tral

Bur

eau

of S

tatis

tics.

***

and

** m

ean

sign

ifica

nt a

t the

1%

and

5%

sign

ifica

nce

leve

l, re

spec

tivel

y.

21

lAg

so

bser

vAti

on

sCP

i ex

Clu

din

g

foo

d &

ener

gy

CPi e

xCl

ud

ing

fu

el

CPi e

xCl

ud

ing

foo

d

CPi e

xCl

ud

ing

en

erg

y

CPi e

xCl

ud

ing

fo

od

& fu

el

CPi e

xCl

ud

ing

9

sd C

oM

Pon

ents

127

40.

588

0.00

0***

0.96

60.

253

0.00

1***

0.00

3***

327

20.

047*

*0.

950

0.00

0***

0.05

7*0.

008*

**0.

240

626

90.

107

0.97

90.

000*

**0.

137

0.00

4***

0.54

5

1226

30.

217

0.99

90.

000*

**0.

295

0.04

7**

0.75

3

2425

10.

656

0.91

00.

006*

**0.

191

0.34

30.

735

lAg

so

bse

rvAt

ion

sCP

i ex

Clu

din

g

4 sd

Co

MPo

nen

ts

CPi e

xCl

ud

ing

11

Pers

iste

nCe

Co

MPo

nen

ts

CPi e

xCl

ud

ing

5

Pers

iste

nCe

Co

MPo

nen

ts

CPi e

xCl

ud

ing

th

e hig

hes

t Pe

rsis

ten

Ce

CoM

Pon

ents

CPit

exCl

ud

ing

Ch

ose

n

CoM

Pon

ents

127

40.

207

0.14

60.

055*

0.02

4**

0.28

0

327

20.

530

0.06

8*0.

217

0.00

0***

0.00

1***

626

90.

505

0.04

3**

0.24

40.

000*

**0.

002*

**

1226

30.

424

0.13

40.

339

0.00

3***

0.02

4**

2425

10.

768

0.26

20.

312

0.03

2**

0.22

8

Tabl

e 4:

Gra

nger

cau

salit

y te

st r

esul

ts fo

r th

e pe

riod

Sep

tem

ber

1992

to J

uly

2015

Sour

ce: A

utho

r’s c

alcu

latio

ns b

ased

on

info

rmat

ion

from

the

Cen

tral

Bur

eau

of S

tatis

tics.

***,

**,

and

* m

ean

sign

ifica

nt a

t the

1%

, 5%

, and

10%

sign

ifica

nce

leve

l, re

spec

tivel

y.Th

e nu

mbe

r of l

ags i

ndic

ates

the

num

ber o

f mon

ths.

22

Allen, Courtney. “Measuring core inflation.” Social and Economic Studies 49, no. 2/3 (2000): 279-312.

Bicchal, Motilal, Naresh Kumar Sharma, and Bandi Kamaiah. “Some Measures of Core Inflation for India.” IUP Journal of Applied Economics 11, no. 3 (2012): 22-64.

Blinder, Alan. “Commentary.” Federal Re-serve Bank of St. Louis Review (1997): 157-160.

Bryan, Michael F., and Stephen G. Cecchet-ti. “Measuring core inflation.” In Monetary Policy, pp. 195-219. The University of Chi-cago Press, 1994.

Chamberlin, Graeme. “Methods explained: core inflation.” Economic and Labor Market Review 3, no. 3 (2009): 48-57.

Clark, Todd E. “Comparing measures of core inflation.” Economic Review-Federal Re-serve Bank of Kansas City 86, no. 2 (2001): 5-31.

Cutler, Joanne. Core Inflation in the UK. No. 3. External MPC Unit Discussion Paper, 2001.

Demarco, Alexander. “A new measure of core inflation for Malta.” Central Bank of Malta Quarterly Review 37, no. 2 (2004): 43-49.

Gordon, Robert J., William D. Nordhaus, and Charles L. Schultze. “The impact of aggre-gate demand on prices.” Brookings Papers on Economic Activity, no. 3 (1975): 613-670.

Huwiler, Marco. “Measures of core inflation in Switzerland: an evaluation of alternative calculation methods for monetary policy.” In 11th Ottawa Group Conference, 2009.

Johnson, Marianne. “Core inflation: a mea-sure of inflation for policy purposes.” In Measures of underlying inflation and their role in the conduct of monetary policy, pp. 86-134. 1999.

Mankikar, Alan, and Jo Paisley. “Core in-flation: a critical guide.” Bank of England Working Paper, no. 242 (2004): 2-36.

Martel, Sylvain. A Structural VAR Approach to Core Inflation in Canada. No. 10. Bank of Canada Discussion Paper, 2008.

Marques, Carlos Robalo, Pedro Duarte Neves, and Luis Morais Sarmento. “Evalu-ating core inflation indicators.” Economic modeling 20, no. 4 (2003): 765-775.

Neves, Pedro Duarte, and Luís Morais Sar-mento. “The substitution bias of the con-sumer price index.” Banco de Portugal, Eco-nomic Bulletin (1997): 25-33.

REFERENCES

23

Office for National Statistics (ONS). “Guide to seasonal adjustment with X-12-ARIMA.” (2007).

Roger, Scott. Core inflation: concepts, uses and measurement. No. G98/9. Reserve Bank of New Zealand Discussion Paper, 1998.

Vega, Juan Luis, and Mark A. Wynne. “An evaluation of some measures of core infla-tion for the euro area.” ECB Working Paper, no. 53 (2001): 3-42.

Wiesiołek, Piotr, and Anna Kosior. “To what extent can we trust core inflation measures? The experience of CEE countries.” In Par-ticipants in the meeting, pp. 297-323. 2009.

Wynne, Mark. “Core inflation: a review of some conceptual issues.” ECB Working Pa-per, no. 5 (1999).

24

APPENDICES

heAdline inflAtion Core inflAtion

Rate of change in the overall price index. Rate of change in the overall price index exclud-ing temporary supply shock components, thereby only measuring demand pressure or permanent components of the overall price index.

Indicator of changes in the cost of living of people in an economy.

Indicator of changes in monetary inflation.

Measures the overall inflation trend. Proxy measure for the underlying inflation trend.

Captures the transitory or noisy part of inflation. Captures a notion of trend inflation.

Constitutes permanent and transitory components of inflation.

Constitutes permanent and not transitory inflation.

Constitutes both the anticipated and unanticipated part of inflation expectations.

Constitutes anticipated components of inflation only.

Appendix 1: Comparison between headline inflation and core inflation

Source: Bicchal et al., 2012

25

exeMPtion ClAuse

Advantages Disadvantages

Simple. Choice of exclusion of certain items in the price index is subjective/arbitrary, i.e., a priori exclu-sion of volatile price components.

Computation in a timely manner. Especially in developing countries, food and en-ergy items comprise the largest share in expendi-tures by the lower income groups. A core inflation measure excluding food (and energy) prices -ac-counting for more than half of the overall price index- is not very meaningful in such a case (Ja-lan, 2002).

Easily understood by the public.

Uses actual price data.

Not subject to significant revisions other than data revisions.

Based on a pre-defined rule, enhancing transpar-ency (Roger, 1998).

Appendix 2: Advantages and disadvantages of the exemption clause

Source: Allen, 2000, Vega & Wynne, 2001, and Bicchal et al., 2012

26

seA

son

All

y A

dju

sted

PriC

e se

rie

s

Gro

up 1

(Sep

t. 19

92-J

an. 1

996)

Gro

up 2

(Feb

. 199

6-Se

pt. 2

006)

Gro

up 3

(Oct

. 200

6-Ju

l. 20

15)

Pric

e co

mpo

nent

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Oct

. 199

0

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Feb.

199

6

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Oct

. 200

6Fo

od

Cer

eal p

rodu

cts

371

232

185

Mea

t & fi

sh

SA66

236

925

1Fa

ts &

coo

king

oils

72

42SA

25D

airy

pro

duct

s (ex

cept

but

ter)

177

124

96Po

tato

es, v

eget

able

s, &

frui

t SA

301

SA23

216

3Su

gar &

cho

cola

te

6245

SA33

Prep

ared

food

SA

4772

61O

utdo

or c

onsu

mpt

ion

161

260

339

Oth

er fo

od

112

9080

Bev

erag

es &

toba

cco

Bev

erag

es

183

192

136

Toba

cco

SA41

4121

Clo

thin

g &

foot

wea

rC

loth

ing

754

607

401

Foot

wea

r16

414

775

Hou

sing

Dw

ellin

g co

sts

SA1,

253

1740

1809

Ener

gy e

xpen

ses

598

371

588

Mai

nten

ance

of d

wel

ling

137

217

258

Gar

den

mai

nten

ance

3968

90W

ater

317

251

339

App

endi

x 3:

CPI

com

pone

nts

27

seA

son

All

y A

dju

sted

PriC

e se

rie

s (C

on

tin

ued

) Gro

up 1

(Sep

t. 19

92-J

an. 1

996)

Gro

up 2

(Feb

. 199

6-Se

pt. 2

006)

Gro

up 3

(Oct

. 200

6-Ju

l. 20

15)

Pric

e co

mpo

nent

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Oct

. 199

0

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Feb.

199

6

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Oct

. 200

6H

ouse

hold

furn

ishi

ng &

appl

ianc

esFu

rnitu

re &

illu

min

atio

n12

316

212

7

Uph

olst

ery

& d

wel

ling

text

ile66

8776

Hou

seho

ld a

pplia

nces

& to

ols

SA11

115

897

Hou

seho

ld a

rticl

esSA

9582

60

Oth

er h

ouse

hold

exp

ense

s11

311

880

Dom

estic

serv

ices

SA54

114

112

3

Oth

er h

ouse

hold

furn

ishi

ng13

013

113

9

Med

ical

car

e

Med

ical

car

eSA

111

203

127

Tran

spor

tatio

n &

com

mun

icat

ion

Tran

spor

t veh

icle

s in

owne

rshi

p*SA

297

571

577

Expe

nses

for o

wn

trans

port

vehi

cles

* (f

uel p

rices

)76

475

573

7

Tran

spor

t ser

vice

sSA

197

311

510

Com

mun

icat

ion

SA28

735

443

9

Rec

reat

ion

& e

duca

tion

Rec

reat

ion

328

451

SA37

1

Ente

rtain

men

t & c

ultu

re72

6469

Boo

ks e

tc.

8580

62Ed

ucat

ion

162

220

242

Hob

by a

rticl

es8

321

App

endi

x 3:

CPI

com

pone

nts

28

seA

son

All

y A

dju

sted

PriC

e se

rie

s (C

on

tin

ued

) Gro

up 1

(Sep

t. 19

92-J

an. 1

996)

Gro

up 2

(Feb

. 199

6-Se

pt. 2

006)

Gro

up 3

(Oct

. 200

6-Ju

l. 20

15)

Pric

e co

mpo

nent

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Oct

. 199

0

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Feb.

199

6

Seas

onal

ity

(SA

=sea

sona

lly a

djus

ted)

Wei

ghts

Oct

. 200

6M

isce

llane

ous

Pers

onal

bod

y ca

re28

626

433

3

Insu

ranc

e31

324

4SA

441

Oth

er c

omm

oditi

es &

serv

ices

460

501

419

Tota

l 10

,000

10,0

0010

,000

App

endi

x 3:

CPI

com

pone

nts

Sour

ce: A

utho

r’s c

alcu

latio

ns a

nd a

ssum

ptio

ns b

ased

on

info

rmat

ion

from

the

Cen

tral

Bur

eau

of S

tatis

tics.

The

CPI

com

pone

nts a

re n

amed

and

cla

ssifi

ed a

s don

e by

the

Cen

tral

Bur

eau

of S

tatis

tics.

*Not

for b

usin

ess u

se

29

Item

Rel

ativ

e im

port

ance

wei

ght O

ct. 2

006

Pric

e in

dex

Oct

. 200

6Pr

ice

inde

x M

ar. 2

008

Upd

ated

rel

ativ

e im

port

ance

wei

ght

Mar

. 200

8

Upd

ated

rel

ativ

e im

port

ance

wei

ght

Mar

. 200

8 (n

orm

aliz

ed)

Gra

in p

rodu

cts

1.85

100.

0011

1.50

=(11

1.50

/100

.00)

*1.8

5 =2

.06

=(2.

06/1

06.2

0)*1

00.0

0 =1

.94

Ove

rall

CPI

100.

0010

0.00

106.

20=(

106.

20/1

00.0

0)*1

00.0

0 =1

06.2

0=N

orm

aliz

ed to

100

.00

App

endi

x 4:

Rel

ativ

e im

port

ance

wei

ghts

Sour

ce: A

utho

r’s c

alcu

latio

ns b

ased

on

info

rmat

ion

prov

ided

by

the

U.S

. Bur

eau

of L

abor

Sta

tistic

s.

The

CB

S pu

blis

hed

the

rela

tive

impo

rtanc

e w

eigh

t per

pric

e co

mpo

nent

on

Oct

ober

199

0, F

ebru

ary

1996

, and

Oct

ober

200

6. T

his m

eans

that

for t

he m

onth

s in

betw

een

thes

e da

tes n

o

data

are

ava

ilabl

e on

rela

tive

impo

rtanc

e w

eigh

ts. A

s a

resu

lt, th

e up

date

d re

lativ

e im

porta

nce

wei

ghts

for S

epte

mbe

r 199

2 to

Jan

uary

199

6 ar

e co

mpu

ted

by u

sing

the

Oct

ober

199

0

wei

ghts

as a

ben

chm

ark.

Sim

ilarly

, the

upd

ated

rela

tive

impo

rtanc

e w

eigh

ts fo

r Feb

ruar

y 19

96 to

Sep

tem

ber 2

006

are

com

pute

d us

ing

the

Febr

uary

199

6 w

eigh

ts a

s a b

ench

mar

k, a

nd

the

upda

ted

rela

tive

impo

rtanc

e w

eigh

ts fo

r Oct

ober

200

6 to

July

201

5 ar

e m

easu

red

usin

g th

e O

ctob

er 2

006

wei

ghts

as a

ben

chm

ark.

The

upd

ated

rela

tive

impo

rtanc

e w

eigh

t for

the

pric

e co

mpo

nent

gra

in p

rodu

cts i

n M

arch

200

8 is

, for

exa

mpl

e, c

ompu

ted

as sh

own

in th

e ta

ble

belo

w.

30

31