The Phillips curve in Thailand¤

Kanyarat BhanthumnavinSt. Antony’s College, University of Oxford

kanyarat.bhanthumnavin@sant.ox.ac.uk

June 13, 2002

Abstract

The subject of this paper is the estimation of the Phillips curve in Thailand.The aim of the estimation is to …t the Phillips curve as part of a small-scale,log-linearised structural model for the country, which is suitable to form the ba-sis for a future simulation exercise to …nd an optimum monetary policy rule. Alinear form of the Phillips curve is estimated using ordinary least squares esti-mation (OLS) under two de…nitions of the rate of in‡ation (a quarterly rate andan annual rate.) The estimation shows that in‡ation dynamics in Thailand isstrongly backward-looking, with the e¤ect of output gap and import price in-‡ation being surprisingly small for such a small, open economy. The resultsalso suggest the hypothesis that the Phillips curve relationship emerged in Thai-land only after the onset of the Asian crisis. This is an interesting hypothesisfor further investigation, because there is no a priori reason why the relation-ship should emerge only after such a strong structural shock to the country’sproductive capacity and its …nancial sector. Further critical discussion on theempirical …ndings might enable us to question the usefulness of the Bank of Thai-land’s in‡ation targeting regime and to question the policy rule currently in use.

Keywords: In‡ation, Phillips curve, ThailandJEL Classification: E31, O51

¤The …rst draft of this paper was presented at the Gorman Workshop Series, Department ofEconomics, University of Oxford, on November 30, 2001. I am grateful for advice and commentsfrom my thesis supervisor Professor David Vines. I also wish to thank Professor Adrian Pagan,Christian List, Christopher Bowdler, Sophocles Mavroeidis, and other participants at the seminar.Special thanks are owed to members of the research department, Monetary Policy Group at the Bankof Thailand, who have given me a great help in getting some data series. All remaining ‡aws aremy responsibility.

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1 Introduction

In response to the …nancial collapse after the Asian crisis, the Bank of Thailandinitiated a drastic reform of …nancial market regulations and of its monetary policyconduct. In particular, the central bank began to make explicit the goal of maintain-ing price and exchange rate stability under the ‡oating exchange rate regime, takingas given the government’s operation of an extensive …scal expansion programme. Thein‡ation targeting regime has been used by the Bank since May 2000 in order to pro-vide the necessary monetary anchor that will aid other structural reforms and tomaintain economic stability conditional on the …scal policy.

One of the key building blocks of an in‡ation targeting regime is a tractablesmall-scale macroeconomic model that can be used to analyse the e¤ects of di¤erentmonetary policy rules on the country’s economic system. It is typical of a centralbank to use a small-scale model for the purpose of policy evaluation in conjunctionwith a larger structural model or a structural vector autoregression (SVAR) model forforecasting purposes. The small model provides speed and tractability in the analysisof the e¤ects of a policy rule at the aggregate level, while a larger model providesmore accuracy in forcasting the di¤erent sectors of the economy at a more disaggre-gated level. Within the academic community, many variants of the small-scale modelare used as a basis for the investigation into issues associated with monetary policyformulation and implementation. For example, Svensson (1997) …rst used a theoret-ical, small-scale model as the constraint functions in his exercise to …nd an optimuminterest-rate-based policy rule in a close economy. Batini and Haldane (1999) usedan open economy version with forward-looking in‡ation and output to examine thee¤ects of using in‡ation forecast as the feedback variable in the policy rule for the UKeconomy. McCallum and Nelson (1999b) studied two theoretical versions of a smallmodel under alternative price-setting behaviour, estimated them for the US economy,and then used the model to study various forms of monetary policy rule.

To use a small-scale model for monetary policy analysis, one can calibrate orestimate the model’s structural parameters. This paper presents, for the …rst time,the estimation of the Phillips curve in Thailand that is suitable to form the basis ofa small-scale structural model for the country. The aim is to study the dynamicsof in‡ation inertia in Thailand during the 1990s, and to investigate the factors thatdrive the in‡ation process. The …rst key issue under investigation is the size of theautogressive lag coe¢cient of in‡ation. This coe¢cient indicates whether the rateof in‡ation in Thailand is strongly determined by its own lag values - i.e. whetherthe Phillips curve is very ‘backward-looking’, or whether it has a forward-looking,expectational property. The identi…cation of the role of in‡ation expectation in thePhillips curve is important not only from a theoretical point of view, where the currentdebate of whether the New Keynesian Phillips curve really exists is still very muchalive, but it is also important for the policy makers to understand the interactionbetween the future and the past value of in‡ation, because this would a¤ect thedynamic structure of an optimum policy rule as well. The problem of identifyingthe e¤ect of in‡ation expectation for Thailand arises because of a lack of data onin‡ation expectation, and on the series of variables that are usually considered to beappropriate ‘instruments’ for it. Given the data constraint, the modelling approach

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used in this paper has to be justi…ed by the properties of the Thai labour market,and the results obtained have to be interpreted carefully.

In this paper we also estimate the size of the coe¢cients and the lag structureof the e¤ect of excess demand, as represented by the output gap, and the openeconomy e¤ect of import price in‡ation in the Phillips curve. These two variablesprovide the channels through which monetary policy instrument, the interest rate,acts as the controller of the rate of in‡ation in a small open economy such as that ofThailand. We found that both variables are signi…cant determinant of the currentrate of in‡aiton, though the size of their coe¢cients are surprisingly small.

In general, the limited number of observations for Thailand poses a challenge tothe general-to-speci…c estimation approach of modelling used in this paper. Thisproblem is further compounded when is it suspected that the structural break causedby the Asian crisis may have an e¤ect on the structural parameters of the Phillipsrelationship. Nevertheless, given the length of the time series a parsimoneous linearmodel of the Phillips curve can still be found for Thailand, although testing for thestructural break resulting from the Asian crisis proves more problematic.

The methodological approach, the estimation results, and the hypothesis testingof the issues discussed above are presented in this paper as follows. After the intro-duction, section 2 lays the background for the discussion about the New KeynesianPhillips curve, particularly about the existing theoretical and empirical debate aboutits forward- versus backward-looking properties. This section also describes the lim-itations of the Thai data set that create di¢culties in the attempt to establish therole of in‡ation expectations in the Phillips curve. Section 3 describes the time seriesproperties of the data and historical facts that a¤ect aggregate demand and supply.Section 4 presents the baseline equations of the Phillips curve used in the estimationand reports the estimated coe¢cients. Section 5 presents the results from testing thehypothesis that the Phillips relationship became visible only after the Asian crisis.Section 6 discusses the tests for linear homogeneity in the rate of in‡ation - whetherthe Phillips curve in Thailand is ‘vertical’. Section 7 then concludes and discussesfuture research prospects.

2 Theoretical Framework: New Keynesian Phillips curvein a small open-economy

2.1 Background and debate

In recent years there has been a gradual convergence among researchers on the un-derlying theory that supports the empirical adequacy of the Phillips relationship. Inparticular, the New Keynesian Phillips curve literature has emerged with a strongsupport from microeconomic foundations. The Phillips curve in these models is de-rived from the optimisation behaviour of rational individual agents, who are assumedto be fully optimising their wage-setting and price-making decision intertemporally.The optimum time path solution of the wage setting process, together with a de…n-able relationship between price-cost mark up, gives rise to a reduced form consideredas the Phillips curve, with a dynamic structure that can be summarised as

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¢lpt = ®¢lpt¡1 +(1 ¡®)Et¢lpt+1 +¯ygapt + °¢lpmt + "¼t (1)

where pt is the log of domestic consumer price level at time t; ygapt;the ‘outputgap’, is the result of subtracting the log of potential output level at time t, y¤t , fromthe log of constant price GDP at time t; yt ;1 and pmt is the log of import priceindex in domestic currency at time t. ¢lpt = pt ¡ pt¡l and ¢lpmt = pmt ¡ pmt¡lthen specify the rates of general price in‡ation and import price in‡ation at l timeinterval. The two variables are quarterly rates of change when l = 1 and are annualrates of change when l = 4: The l = 1 subscripts will be dropped hereafter in thede…nition of every quarterly rate of change variable for ease of exposition.

The central feature of the New Keynesian Phillips curve analysis is that, even ina model where agents are optimising rationally in their wage and price adjustment,there are nominal rigidities within the economy that do not allow them to adjust wagesand prices immediately as shocks occur. These rigidities give rise to the trade o¤between in‡ation and excess demand in the short run, which in turn opens a channelthrough which monetary policy can a¤ect real variables. The New Keynesian analysisconsiders ‘staggered’ nominal adjustment that arises from physical time constraint,such as the fact that wage contracts are adjusted at infrequent time intervals, or withina state-dependent constraint, such as when …rms only adjust prices once certain costscondition are satis…ed. In particular, because of the nominal rigidities the currentrate of in‡ation is determined by its lag (¢lpt¡1) as well as its future expectations(Et¢lpt+1). The theoretical analyses of this underlying wage-setting behaviour aregiven in the seminal work by Taylor (1980), whose staggered price adjustment processis deterministic, and by Calvo (1983), whose staggered price adjustment behaviouris stochastic. The discussion below focuses only on the deterministic type of priceadjustment in order to illustrate the current debate about about the importance ofin‡ation expectations in the dynamics of the Phillips curve.

In Taylor’s (1980) simple staggered wage contract model, in any one time periodonly a fraction of agents are able to adjust their wages, and the rest of them have towait until the following period before they can make the adjustment. This conditiondirectly implies that prices are not only autoregressive, but are also dependent on fu-ture expectation. It can then be shown that the current rate of in‡ation also dependson the expected rate of in‡ation in the future. Rotemberg and Woodford (1999) andMcCallum and Nelson (1999a) explicitly specify an individual’s optimisation prob-lem in order to derive the same conclusion that the dynamics of the current rate ofin‡ation is governed by its expected values. In this theoretical representation, thevalue of ® is zero, and current in‡ation are determined with no reference to laggedin‡ation values.

A di¤erent wage-contracting model by Fuhrer and Moore (1995) emphasises astronger inertia of past price adjustment in in‡ation dynamics. In particular, Fuhrer

1 There are several methods that can be used to construct the output gap term; all of which arecontentious, and each has di¤erent strengths and drawbacks. It is not the purpose of this paper tostudy this issue in detail, although the choice of detrending used for Thailand is explained brie‡y inAppendix A. Nevertheless, I acknowledge that the choice of detrending for the output gap can havesigni…cant e¤ects on the estimation of the model.

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and Moore argued that if workers care about relative real wages rather than nominalwages, there will be a proportion of past in‡ation left in the dynamics that governsthe current value, and ® will take a value between zero and one. While emphasis-ing that the empirical evidence strongly supports the existence of in‡ation inertia,and hence the theoretical model should account for this fact, their model still re-tains some degree of the forward-looking character of the in‡ation dynamics. Someempirical studies of the US economy, such as one by Gali and Gertler (1999), haveattempted to estimate this form of the Phillips curve that is both forward-lookingand backward-looking. They concluded that both the forward- and backward-lookingcomponents are signi…cant in the determination of current in‡ation, thus supportingthe hypothesis that ® takes a value between zero and one.

On the other hand, other studies of monetary policy rules using a small-scalestructural model are based on the assumption that the Phillips curve is entirelybackward-looking, i.e. they assume that ® = 1. (for example, Bean (1998), Svensson(1999), and Ball (1999)) Although some recent empirical studies of the US Phillipscurve (Rudd and Whelan (2001) and Roberts (2001)) show that some serious doubtsshould be cast upon Gali and Gertler’s …ndings that the forward-looking part of thedynamics is more signi…cant than the inertia , and that the entirely backward-lookingPhillips curve hypothesis is compatible with the US evidence, the theoretical intuitionbehind the assumption of an entirely backward-looking in‡ation process is not fullysatisfactory. It does not seem plausible that agents look only at ‘the past’ when theymake wage and price adjustments. Given that rational agents do ‘look forward’ whenthey make decisions, we would expect to see an in‡ation expectation component inthe Phillips curve, together with the lagged in‡ation component, which representsthe inertia that arises from the staggered nominal adjustment.

2.2 Estimating the New Keynesian Phillips curve for Thailand

The important issue in estimating a New Keynesian Phillips curve is the identi…cationof the true e¤ects that lagged in‡ation and in‡ation expectation have in determiningthe current rate of in‡ation. As discussed earlier, there is still a very lively debatewithin the empirical literature on the Phillips curve about the size of the coe¢cientof in‡ation expectation in its dynamics.

Ideally, I would like to estimate the New Keynesian Phillips curve as speci…ed in(1), which encompasses both the forward-looking and backward-looking components,as it is better to allow the estimated Phillips curve to take the most general formwithout strong a priori assumptions about its dynamics. However, there are limi-tations in the Thai data set that do not allow a direct incorporation of a proxy forin‡ation expectation. Because no business survey of in‡ation expectation has beenconducted until the …rst quarter of 2000, the historical time series of such a variableis not at all available. Although Debelle and Vickery (1998), and Gruen, Pagan,and Thompson (1999), used the di¤erence between the domestic long-bond interestrate and the world’s equilibrium interest rate as an alternative proxy for in‡ationexpectation, this mothod, unfortunately, can not be applied to Thailand because themarket for government bonds in the economy was almost non-existent during the

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long-period of …scal surplus between the late 1980s and the Asian crisis in 1997.2

Nevertheless, there might be some justi…cation for estimating a purely backward-looking version of the Phillips curve for Thailand when we look more closely at therelationship between the dynamics of wage setting and that of prices. To considerthis claim, it is useful to think of in‡ation dynamics as a reduced form of nominalwage dynamics because the price level can be represented as a mark-up of wagecosts. Under the assumption about staggered wage contracting in Taylor’s (1980)or Fuhrer and Moore’s (1995) model, it is logical to assume that, for workers whosewage contracts are ‘sunk’ for a long period of time, for example for 2-3 years, thee¤ect of the forward-looking element in wage determination is signi…cant because theworkers have to account for the decline in their real wages resulting from the increasein the price level throughout the whole period of the contract.

However, if the frequency at which wage contracts are renewed in Thailand ismuch shorter than the period generally found in the OECD countries, the impact ofthis forward looking element in in‡ation dynamics may be much smaller than thatanticipated by the theoretical model. This claim is hard to verify because therehas never been an empirical study of wage contracting in Thailand. Nevertheless,a casual observation of wage contracts for the medium and low-skill workers in themanufacturing and service sectors would not strongly dispute the claim that thelength of their contracts is shorter than the one observed in the OECD countries. Amore detailed study of the Thai labour market will enable me to clarify this issuefurther.

Another possible approach of estimation that may be used to address this problemis Instrumental Variable Estimation (IVE). It is possible to consider the lag valuesof exogenous variables, such as wholesale price index, oil price, or manufacturingproduction capacity, as instruments for in‡ation expectation. However, there aretwo immediate problems associated with using IVE. First is the fact that, becausethere is no direct observation of in‡ation expectation, in order to ‘instrument’ for itwe would need to make some assumption about the expectation process in the Thaieconomy, as this would allow us to use the actual future values of in‡ation as thevalue of in‡ation expectation itself. For example, if we assume that agents haverational expectation, we could use the relationship

¼et+1 = Et¼t+1 = ¼t+1 + º t+1 (2)

where Et¼t+1 represents the expected value in period t of in‡ation in periodt + 1, ¼et+1 is in‡ation expectation, ¼t+1 is the actual value of in‡ation in periodt + 1; and ºt+1 is expectational error, assumed to be perfectly random with zeromean. Under this assumption, we can use the actual value of in‡ation in period t+1

2 From 1988 to 1996, the Thai government had been operating a policy of …scal surplus. As aresult, the government only issued long-term bonds at infrequent time intervals. A large proportionof the existing bonds owned by …nancial institutions was also required to be held at the central bankas part of the reserve requirement. Consequently, during the period of investigation the volume oftrade in the repurchase market of long-term government bonds was so low, such that the Bank didnot have any formal record of the data series before 1997.

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as the forward-looking value ¼et+1 in equation (1); if agents have rational, ‘model-consistent’ expectations. The expectational error term ºt+1 then gets absorbed intothe regression error term when (2) is substituted into (1). However, given that theThai economy has just undergone a major policy change in the early 1990s, it may betoo demanding to assume that agents have a stable expectational process with regardto the level of economic activity under this policy. Equivalently, any assumptionregarding the process of making expectation in the economy during the this time canbe criticised under the same reservation.

The second problem associated with the IVE approach is the identi…cation prob-lem in distinguishing between the direct and indirect e¤ects of the variables used asinstruments. It is conceivable that the variables used as instruments for in‡ationexpectation themselves have a direct in‡uence on the current rate of in‡ation. Thuswhen they are used as instruments, and substituted for in‡ation expectation in equa-tion (1), the resulting coe¢cient may get its signi…cance from the direct e¤ects ofthe instrument on current in‡ation rather than the e¤ects of in‡ation expectation oncurrent in‡ation. Rudd and Whelan (2001) discusses and shows this identi…cationproblem in greater details for the US economy.

Given all the caveats of using alternative approaches to model in‡ation expec-tations, for the purpose of estimating the Phillips curve given such data limitationdiscussed above, I feel that the backward-looking speci…cation is su¢ciently justi…ed.The search for a suitable proxy for in‡ation expectation in the Thai economy, or asuitable alternative way of incorporating in‡ation expectation into the model whenit cannot be explicitly estimated, is the subject of ongoing work. For now, the es-timation of the Phillips curve for Thailand, as discussed in section 4 below, is basedon an entirely backward-looking speci…cation.

3 The Thai data

The most prominent problem of this estimation exercise is the short time-span natureof the data. Generally, the annual observations for most series are available for along time span, usually dating back to the 1970s. However, monthly and quarterlydata series are more di¢cult to obtain because the data collection methodology hasnot been fully developed until the 1990s. In particular, quarterly gross domesticproduct series have been available only since 1993. So although the consumer priceand import price indices are available in quarterly observations for a much longerperiod, the lack of historical GDP data e¤ectively sets the limit of the historical timespan of the data set used in this estimation.

Starting the study of Thailand’s structural model only in 1993 also makes sensewhen we consider historical structural changes in the economy. In 1990, the Bankof Thailand embarked on a three-year plan that saw major …nancial liberalisationmeasures being implemented in order to allow the Thai capital market to develop.In particular, the central bank emphasised the ‘big bang’ approach of lifting all atonce the interest rate ceilings, the exchange rate and capital account control, and theregulations concerning international banking services o¤ered by …nancial institutions.This means that by the end of 1992, …nancial market structure and the behaviour

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of agents throughout the economy became very di¤erent from the previous decadeswhere the country was still subject to rigid …nancial rules.

A historical plot of key macroeconomic variables used in this estimation is pre-sented in Figure 1 and 2. Figure 1 shows the actual time series in level of thevariables, and Figure 2 shows the plot of the variables that enter the Phillips curve asdiscussed above. At …rst glance, it appears that there were very little variations inthe annual growth rate of the import price index, de…ned as ¢4pmt = pmt ¡ pmt¡4,during the period before 1994Q2, whereas the annual rate of in‡ation at time t, de-…ned as ¼t = ¢4pt = pt ¡ pt¡4; has a slight upward trend during the same period.From 1995Q1 onwards, however, the two series move pretty much in the same direc-tion. The output gap series, on the other hand, hovers around its zero mean duringthe period 1993-1995, until 1996 when it started a downward trend. Two other dataseries that are of interest are fuelt; the log of fuel price index, which includes pricesof di¤erent kinds of petrol and lubricant in period t, and ulct; the log of nominalunit labour cost in baht in period t. These series are considered as components oftotal unit costs which, under a mark-up assumption, constitute the general price levelseries.

Augmented Dickey-Fuller (ADF) unit root tests of all the series give the resultsin Table 1 below

Table 1 : ADF unit root testsVariables Order of integration Order of ADF test ADF test statistics¢¼ I(0) 1 -3.2455*¢2p I(0) 2 -3.865**¢ygap I(0) 2 -3.6853**¢2pm I(0) 2 -4.0742**¢fuel I(0) 1 -3.325*¢ulc I(0) 1 -5.139**

* and ** denote signi…cance at 5% and 1% level respectively; critical values aretaken from MacKinnon (1991).

Unit root tests for the variables that appear in the Phillips curve estimation areconducted with the break in the middle of the data series (resulting from the Asiancrisis) deliberately left in, i.e. Perron (1989)’s type of tests which would have purgedout the break is deliberately bypassed. Without the Asian crisis period taken out,in‡ation and output gap are found to be I(1) variables. This seems counter intuitivein the …rst instance because economic theories predict that the output gap shouldhave a zero mean in the long run, when actual output has had time to adjust back toits underlying supply side value. Similarly, in‡ation should also have a constant meanvalue in the long run otherwise hyperin‡ation situations would ensue. However, theI(1) property emerges largely because the large and persistent variations of the twoseries around the time of the Asian crisis were allowed to be present in the data. Thejusti…cation for this seemingly controversial speci…cation is given below.

After an inspection of the output gap, import price in‡ation, and general pricein‡ation series (Figure 2), it is clear that there were not much variations in both the

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1990 1995 2000

100

120

Core consumer price index

1990 1995 2000

100

150

200 Pm

Px

Import and Export price indices

1990 1995 2000

0.1

0.2

Interbank

1-Day Repurchase rate

Nominal interest rate (fraction)

1990 1995 2000

13.3

13.4

13.5

13.6 log of actual and potential output potential

actual

1990 1995 2000

100

200

300 Fuel Price index

1990 1995 2000

-0.05

0.00

0.05

0.10Unit labour cost (seasonally-adjusted)

Figure 1: Level of key macroeconomic variables

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1990 1995 2000

0

.01

.02

Quarterly rate of inflation

1990 1995 2000

.025

.05

.075

Annual rate of inflation

1990 1995 2000

-.1

0

.1

.2Quarterly rate of import price inflation

1990 1995 2000

-.25

0

.25

.5 Annual rate of import price inflation

1990 1995 2000

-.1

0

Output gap

1990 1995 2000

-.1

0

Output gap

Figure 2: Quarterly and annual rates of general price in‡ation and import pricein‡ation, and the output gap

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output gap and import price in‡ation series, the key variables that ‘drive’ the Phillipsrelationship, during the period 1993-1995. By crude inspection, it would be veryhard to …nd a Philips relationship before the onset of the Asian crisis, and in fact thePhillips relationship can be seen only after the crisis. That is, it is not unreasonableto think of the Asian crisis as the source of variations in in‡ation, output, and importprice in‡ation in Thailand just like other events that cause economic ‡uctuations,which allow us to …nd the underlying Phillips relationship that exists in the economy.

The key point that should be carefully noted is that the Asian crisis may have alsocaused structural changes in the underlying economic relationship itself. That is, theevent may generate not only variations in the economic variables, but also changes inthe underlying structural parameters of the model. Ideally, this hypothesis should betested by parameter constancy tests such as the Chow test, and in a certain fashion,section 5 of this paper attempts to do just that. However, because there were verylittle variations in the variables before the Asian crisis, the estimated parameters areall signi…cantly near zero for that period. In other words, even though there wasan underlying Phillips relationship in Thailand before the Asian crisis, there was novariation in the data series during the period that would allow the relationship tobecome visible.

The criticism that the underlying structural changes resulting from the Asian cri-sis may not be well represented by this consideration of the data series is neverthelesspartially addressed by the modelling approach in this paper. In particular, thestructural changes, at least on the supply side, have been taken into account by theconstruction of the natural rate of output variable itself. This variable is computedfrom the production function principle, and hence does take into account the under-lying changes in the production capacity of the economy via the changes in factorinputs (such as rising unemployment, collapsing investment) that result directly fromthe Asian crisis. We still have to assume that the structural parameters of the de-mand side of the economy remain the same before and after the crisis. Nevertheless,given this data limitation, the postulated hypothesis does not seem too unreasonable.

4 Estimation

4.1 Econometric Methodology

Three baseline speci…cations of the Phillips curve were considered. These are in anequilibrium-correction form and do not have the forward-looking component. Allthree equations were …rst …tted with the most general lag speci…cation permitted bythe data set, and then reduced down to the most parsimonous speci…cation by theelimination of insigni…cant regressors.

¢pt = ®0 +4Pi=1

®i¢pt¡i +4Pj=0

¯jygapt¡j +4Pk=0

°k¢pmt¡k ¡ ÃECMt¡h+ "1t (3)

¼t = ®0 +3Pi=1

(1 +®i)¢pt¡i +®4¢pt¡4 +4Pj=0

¯jygapt¡j +4Pk=0

°k¢pmt¡k ¡ ÃECMt¡h+ "1t

(4)

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¼t = ®¤0+3Pi=1

®¤i¼t¡i +4Pj=1

¯¤jygapt¡j +4Pk=1

°¤k¢4pmt¡k ¡ÃECMt¡h + "2t (5)

where pt; ygapt; pmt and their rates of growth (where applicable) are de…ned asbefore. ECMt¡h the equilibrium correction term in period t ¡ h where h = 1::4, isspeci…ed as

ECM = p¡ �pm ¡¸fuel ¡ !ulc (6)

where fuel and ulc are de…ned above. The inclusion of the ECM term allows usto test two hypotheses. First, we can test for the hypothesis that, in the long run,domestic general price level is a mark-up over total unit costs of production, whichconsist of the costs of imported intermediate goods, energy used in the productionprocess, and labour cost. If this hypothesis of linear homogeneity in the generalprice level holds, then the coe¢cients �+ ¸ + ! should add up to 1. The secondhypothesis that we can test is that, in the long run, the rate of in‡ation has anadjustment mechanism that brings it back in line with the equilibrium price level,which is represented by the linear homogeneity concept. If the long run equilibriumadjustment process exists on top of the short run adjustment due to the size of theoutput gap and changes in import price in‡ation, then the coe¢cient à should besigni…cant. This is a simple one-step hypothesis testing due to Kremers, Ericssonand Dolado (1992)’s test of cointegration in a single equation with weakly exogenousregressors. Under the general-to-speci…c modelling approach, lags 1 to 4 of the ECMterm were individually entered into the regression in order to see which lag gives themost signi…cant result for the à coe¢cient.

Equation (3) speci…es the Phillips curve using the quarterly rate of price in‡ation(¢pt) as being determined by its own lags and the current value and lags of importprice in‡ation (¢pmt¡k). Both equations (4) and (5) specify the annual rates of pricein‡ation (¼t = ¢4pt) as the dependent variable; equation (4) uses quarterly ratesof in‡ation in the general price level and import price level as regressors, whereasequation (5) uses the lagged annual rates of change as regressors. The speci…cationof equation (4), …rst suggested by Gruen, Pagan, and Thompson (1999), is equivalentto modelling the quarterly rate of in‡ation in the Phillips curve. To see why (4) isan isomorphic transformation of (3), …rst note that

¼t = pt ¡ pt¡4 = ¢pt + ¢pt¡1 +¢pt¡2 + ¢pt¡3 (7)

In equation (3), we could add ¢pt¡1 +¢pt¡2+ ¢pt¡3 to the dependent variable,which will tranform it to the annual rate of in‡ation. Then by adding the same set ofvariables to the right hand side to balance the equation, the regressors ¢pt¡i(i = 1:::3)will now have coe¢cients (1+®i). In fact the regression results of (3) and (4) shouldbe equivalent because equation (4) is just an isomorphic transformation of equation3).

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I am considering all three speci…cations to allow for the possibility that empiricallythe Phillips curve speci…ed with a quarterly rate of in‡ation might give a better …tthan the one speci…ed with the annual rate of in‡ation; hence both equations (3) and(5) are …tted and then compared. This decision will become important for futuremodelling purposes, as I have to consider the choice of endogenous variables in thestructural model that will appear in both the Phillips and the IS curves.

Fitting equation (4) has several advantages under this consideration. First, itenables us to consider as dependent variable the annual rate of in‡ation, which is thetarget rate for the in‡ation targeting regime, while allowing for the possibiliy that thebest-…tting relation involves an explanation of the quarterly rate. Moreover, underan appropriate linear transformation similar to the one discussed above, namely touse ¼t¡1 = pt¡1¡pt¡5 = ¢pt¡1+¢pt¡2+¢pt¡3+¢pt¡4 I can consider whether thereis an adequate justi…cation for reducing a quarterly in‡ation Phillips curve to the onespecifed by the annual rates of change. This is so because if ¢pt¡4 is a signi…cantdeterminant of the annual rate of in‡ation ¼t in equation (4), and if ®i (i = 1:::4) alltake a similar value, then the quarterly rate of in‡ation can be reduced to ¼t¡1by thetransformation shown above.

The congruency of the regressions is checked by a set of diagnostic tests for theproperties of the residuals. The values of residual standard deviation (Sigma) and R2

are reported, along with three diagnostic tests for the regression residuals under threenull hypotheses; that a) there is no residual autocorrelation (AR F-test), b) that theresiduals are normally distributed (Â2 Normality test), and c) that the residuals arehomoscedastic (F test using squared residuals). The probability of obtaining the teststatistics or larger values under each null hypothesis is reported in square bracketsnext to the test statistics. We reject the null hypotheses if these probabilities fallbelow 5% or 1% signi…cance level. The quarterly data set for the period 1993Q4 -2000Q4 is used for the regression unless speci…ed otherwise.

4.2 Baseline results

The coe¢cient estimates of the three baseline equations are reported in Table 2 below.

13

Table 2 : Three baseline speci…cationsEQ(3) EQ(4) EQ(5)

Dependent ¢pt ¼t ¼tconstant 0.005 0.004 0.004¢pt¡1 0.544 (4.07) 1.468 (8.66) –¢pt¡2 ** 1.101 (5.31) –¢pt¡3 ** 1.080 (6.38) –¢pt¡4 ** ** –¼t¡1 – – 0.898 (13.51)

ygapt¡2 0.038 (2.67) 0.034 (2.25) **ygapt¡3 ** ** 0.045(2.17)¢pmt 0.025 (2.35) 0.031 (2.44) –¢4pm – – 0.037 (5.54)ECM ** ** **

R2 0.676 0.967 0.936Residual s.e. 0.0042 0.0043 0.006AR 1-4 (F) 0.714 [0.59] 1.019 [0.42] 1.343 [0.29]

Normality (Â2) 2.20 [0.33] 2.44 [0.29] 1.98 [0.39]Hetero (x2) 0.26 [0.94] 0.222 [0.98] 0.41 [0.86]

D.o.f. 29 ¡ 4 = 4 29 ¡ 5 = 23 29 ¡ 4 = 24

** denotes non-rejection of H0 : the co¢cient is zero

The coe¢cients on lagged in‡ation, output gap, and import price in‡ation allhave the expected signs. As discussed earlier, equation (3) and (4) are isomorphicso we would expect the coe¢cients to be similar in size and sign. However, whenwe subtract ¢pt¡i(i = 1:::3) from both sides of equation (4), the results suggestthat lag 2 and 3 of the quarterly rate of in‡ation are signi…cant in determining thecurrent rate, with coe¢cients > 0; in contrast to the results found in equation (3).Nevertheless, the lag structure and the size of the coe¢cients of the output gap andquarterly import price in‡ation terms remain the same for equations (3) and (4).

We also found that the coe¢cient à of the ECM term is insigni…cant in all threeforms regressions. This is taken as the indication that the long run adjustment to-wards price level homogeneity underlied by the price-cost mark-up hypothesis cannotbe found in Thailand. This conclusion may seem puzzling since in most studies ofthe Phillips curve in the OECD countries, such long run adjustment process is oftenfound to be signi…cant. But for Thailand the explanation may lie in the institutionalfactors of the Thai labour markets and the retail sectors, which are very di¤erent fromthose of the OECD countries. This implies that in Thailand the mark-up processitself may require a di¤erent kind of modelling from those for the OECD countries.However, since testing and modelling for the mark-up hypothesis is not my main ob-jective in the estimation of the Phillips curve in Thailand, the analysis regarding thisresult is left for future research. A similar argument can also be given for testing thehypothesis of linear price homogeneity itself.

Figure 3 shows the plots of the actual and …tted values of the rate of in‡ationas speci…ed by the three equations. From casual observation, regression (3) seems

14

1994 1995 1996 1997 1998 1999 2000 2001

0.00

0.01

0.02

0.03EQ(3)

DLCPI Fitted

1994 1995 1996 1997 1998 1999 2000 2001

0.025

0.050

0.075 EQ(5)

INFLA Fitted

1994 1995 1996 1997 1998 1999 2000 2001

0.000

0.025

0.050

0.075 EQ(4)

INFLA Fitted

Figure 3: Actual and …tted values of the three baseline regressions

to give the worst …t while regression (4) gives the best …t. This is perhaps notsurprising because there are bound to be more noises in the quarterly rate of changeseries than in the annual rate of change series, which, by de…nition, already have somenoises removed by averageing. This is precisely the advantage that …tting regression(4) has; the dependent variable in this regression, the annual rate of in‡ation ¼t;

already has some noises removed, while the regressors ¢pt¡i(i = 1::3) still retainextra information in their measurement. In fact, regression (4) seems to give thebest description of the Phillips curve in Thailand.

Regression (5) also …ts the data quite well, especially for the period from 1997Q1onwards. As explained in section 4.1, the purpose of comparing regression (4) withregression (5) is to verify whether a Phillips curve speci…ed with a quarterly rateof in‡ation …ts the data better than the one that uses the annual rate. From thegraphical comparison and observations of diagnostic tests for the two regressions,regression (4) …ts slightly better, with a higher R2 and lower regression standarddeviation. However, it could be argued that the advantage of using regression (5)in the small-scale macro model, which is the main objective of this estimation exer-cise, outweighs the cost in the reduction of goodness of …t. In regression (4) thereis a mixed speci…cation between annual and quarterly rate of in‡ation, whereas inregression (5) the rate of change of the dependent and indepent variables are bothspeci…ed in annual rates. This makes the speci…cation of the model tidier, as theautoregressive process of in‡ation is explained by the same variable. Moreover, the

15

reduction in the goodness of …t of regression (5) compared with the …t of regression(4) is acceptable. Hence, for the future purpose of using the estimated Phillips curvein a small-scale simulation framework, regression (5) will be used.

The most striking result that emerges from equation (5) is the strong in‡ationinertia: 89% of the current in‡ation rate is determined by its own lag. The keyimplication from an in‡ation process with such a long ‘memory’ is that the e¤ects ofchanges in the output gap and import price in‡ation, as well as other supply shocks,on the annual rate of in‡ation last for a very long time. For example, if there is a oneunit demand shock to the output gap in period 0, after 11 quarters (2 years plus the3-period lag of output gap e¤ect) its e¤ect on the current rate of in‡ation throughin‡ation inertia alone will still be (0:9)8 = 0:43 multiplied by the coe¢cient on theoutput gap term. That is, as much as 43% of the original shock still remains. Thislong autoregressive lag in the Phillips curve will be very important in the contextof designing monetary policy rule that maximises social welfare under an objectivefunction where the deviation of in‡ation and output from their respective targetsare counted as welfare losses. This welfare e¤ect will be compounded further ifthe IS curve also has a large autoregressive coe¢cient. The key implication of thisobservation on monetary policy making is that the Thai monetary authority will haveto use a long forecasting horizon when implimenting their policy rule.

Another implication of this strong inertia is that although the actual coe¢cientsof the output gap term and the import price in‡ation term are very small (a 1%temporary shock in the output gap will cause only a 0.03% change in the rate ofin‡ation), this seemingly small e¤ect can still drive the whole of the in‡ation processbecause of this very long ‘memory’ in the in‡ation dynamics itself.

However, there is one reason why one should be sceptical about the large au-toregressive coe¢cient of the rate of in‡ation. It is easily noted that, due to thelack of observations in any variable that can be used to infer the level of in‡ationexpectation, the speci…cation of the empirical Phillips curve for Thailand is entirelybackward-looking. This implies that if in‡ation expectation was actually a determi-nant of the current rate of in‡ation in Thailand, its e¤ect would be forced to showvia the lagged value. Furthermore, it is possible that individual agents use the pastrate of in‡ation as a proxy for their expected level of in‡ation in the future. If thishypothesis is true, then the 0.89 coe¢cient could be an amalgam of the direct e¤ectof lagged in‡ation in determining the current value, and the indirect e¤ect of laggedin‡ation being used as an indicator of in‡ation expectation, which also enters theequation as another determinant of the current rate of in‡ation.

The result from regression (5) also seems to suggest a near-vertical Phillips curvefor the annual rate of in‡ation, and this is illustrated in Figure 4 by the scatterdiagram of ¼t against ygapt. First, it is important to note that although the …ttedstatic regression line seems to be very ‡at, the co-ordinates can be roughly dividedinto two groups. One group of the co-ordinates clusters very closely around theregression line that seems to suggest that the non-accelerating-rate-of-in‡ation levelof output gap is a large positive number. But note that these points fall into theperiod 1993-1996, where there was very little variation in both the output gap andimport price in‡ation, i.e. pre-1997. (see the sub-sample analysis below) The othergroup of co-ordinates appears to cluster around a vertical Phillips relationship, and

16

-.2 -.175 -.15 -.125 -.1 -.075 -.05 -.025 0 .025 .05

.01

.02

.03

.04

.05

.06

.07

1998Q3

1998Q2

1998Q1

1998Q4

1999Q2

1999Q32000Q2

1999Q4

2000Q3 2000Q4

2000Q1

1999Q1

Output gap

Annual rate of Inflation

Figure 4: A scatter plot of the annual rate of in‡ation against the output gap

17

are exclusively points from the period 1998-2000. The discussion about testing thishypothesis is explained more in detail in the following section.

5 Is there a Phillips curve before 1997?

The next investigation into the Phillips relationship in Thailand is to ask what rolethe Asian crisis played in the economic dynamics of the country. A …rst inspectionof the time series plot of in‡ation, output gap, and import price in‡ation under theannual measurement in Figure 2, tells us that there was not much variation of theoutput gap and import price in‡ation series during the period between 1990 and theend of 1996, while the movement in in‡ation has a slight upward trend. Furthermore,output gap started to fall only in the period immediately before the Asian crisis. Soa further investigation of whether the baseline results above apply equally to theperiod before and after the crisis would be an interesting exercise. Regression (5) isnow re…tted for each sub-sample: 1993Q1-1996Q4 and 1997Q1-2000Q4. Because ofthe very short overall time span of the data, it is not possible to investigate a longerlag structure of the equation as we have to take into account the …rst few points ofobservation that are lost when lagged values are included. The regression results ofequation (5) under the two di¤erent subsamples are reported in Table 3 below.

Table 3: Sub-sample regressions EQ(5)Period 1993Q4 - 1996Q4 1997Q1 - 2000Q4

Dependent ¼t ¼tconstant 0.034 0.001

¼t¡1 0.2779 (0.73) 0.890 (12.72)ygapt¡3 0.022 (0.50) 0.031 (0.96)¢4pmt 0.036 (1.11) 0.040 (5.44)

R2 0.313 0.963AR 1-4 (F) 15.349 [0.005]** 5.640 [0.019]*

D.o.f 13 ¡ 3 = 10 15 ¡ 3 = 12*rejects at 5% con…dence level the hypothesis H0 : the residuals are not autocor-

rellated**rejects the null hypothesis at 1% con…dence level

The …rst column shows the regression during the period before the Asian crisis.It is clear from the t-statistics, R2 value, and the F-test for residual autocorrelationthat a stable regression for the Phillips relationship can not be found during thisperiod. Column two shows a set of coe¢cients that are in fact very similar to theones obtained from the regression using the full sample set. Although the speci…cationof the equation is not as good as the baseline speci…cation using the whole sample(t-values are very low, residuals may be autocorrelated), this is not surprising giventhe limited number of observations from the period. When we look at the plot inFigure 5 part A and B of the actual and …tted values of in‡ation in the two subsampleregression, it is very clear that there is no …t during 1993-96 and the baseline resultsof equation (5) must be driven largely from the period after the Asian crisis.

18

1994 1995 1996 1997

0.040

0.045

0.050

0.055

0.060

0.065A EQ(5) 1993-1996

INFLA Fitted

1997 1998 1999 2000 2001

0.025

0.050

0.075 B EQ(5) 1997- 2000 INFLA Fitted

Figure 5: Subsample …tting of EQ(5)

Next we consider the regression of the baseline equation (5) for the whole periodof 1993Q4 - 2000Q4, but with dummy variables that separate the e¤ects of outputgap and import price in‡ation for the two periods.

Let D1 = 1 for t � 1996Q4 and zero otherwise, and D2 = 1 for t ¸ 1997Q1 andzero otherwise. I consider two regression equations,

¼t = ®¤0+3Pi=1

®¤i¼t¡i+3Pj=0

¯¤1jD1ygapt¡j +3Pj=0

¯¤2jD2ygapt¡j +3Pk=0

°¤k¢4pmt¡k + "2t

(8)

and

¼t = ®¤0 +3Pi=1

®¤i ¼t¡i +3Pj=0

¯¤jygapt¡j +3Pk=0

°¤1kD1¢4pmt¡k +3Pk=0

°¤2kD2¢4pmt¡k + "2t

(9)

In equation (8) the e¤ects that output gap has on in‡ation in the two periods arecontrolled for by the two dummy variables, while the annual rate of import price in‡a-tion still enters the regression undivided. Equation (9) takes the opposite approach,allowing the e¤ect of import price in‡ation to be separated in the two periods. Theestimated coe¢cients are reported in Table (4) below, and the plots of the actual and…tted values of equations (8) and (9) are shown in the top and bottom panels Figure6 respectively.

19

Table 4: Full sample with Dummy variablesEQ(8) EQ(9)

Controlling for output gap import price in‡ationDependent ¼t ¼tconstant 0.005 0.004

¼t¡1 0.844 (12.92) 0.874 (15.51)¢4pmt 0.035 (5.34) –

D1¢4pmt – 0.082 (3.08)D1¢4pmt¡2 – -0.147 (-3.12)D1¢4pmt¡3 – 0.085 (2.14)D2¢4pmt – 0.037 (6.32)ygapt¡3 – 0.045 (2.43)

D1ygapt¡3 ** –D2ygapt¡3 0.050 (2.29) –

R2 0.937 0.958Residual s.e. 0.0058 0.0050AR 1-4 (F) 1.199 [0.34] 2.456 [0.08]

Normality (Â2) 1.57 [0.45] 0.64 [0.73]Hetero (x2) 0.08 [0.99] 0.71 [0.71]

Degrees of freedom 29 ¡ 4 = 25 29 ¡ 7 = 22** denotes non-rejection of H0 : the co¢cient is zero

The results from regression (8) show that the estimated coe¢cients for ¼t¡1;D2ygapt¡3,and ¢4pmt are very similar to the estimated coe¢cients of ¼t¡1; ygapt¡3; and ¢4pmt

from equation (5) using the full sample (see Table 2). This, together with resultsfrom the 1997-2000 subsample estimation, supports the hypothesis that the main re-gression equation is mostly driven by the dynamics in the period around which andafter the Asian crisis has occured.

The results from regression (9) when the e¤ect of annual import price in‡ation iscontrolled by dummy variables D1 and D2 are shown in the second column. Againthe coe¢cient on ¼t¡1 is only slightly di¤erent from the corresponding coe¢cient esti-mated from the regression of equation (5) using full sample; the coe¢cient of ygapt¡3remains almost the same, and the coe¢cient of import price in‡ation controlled by the1997-2000 dummy increased only slightly compared with that of the baseline equationusing a full sample set. This result again con…rms that the overall Phillips curveis largely driven by the dynamics during the post-crisis period. However, now thecoe¢cients for the e¤ects of import price index in the …rst period become much largerfor ¢4pmt and ¢4pmt¡3, and the coe¢cient of ¢4pmt¡2 now takes a negative sign.This pattern suggests that import price in‡ation does a¤ect the rate of in‡ation witha di¤erent dynamics during the pre-crisis period, but because there was no outputgap e¤ect, it is not clear what underlying mechanism was at work during that time.

20

1994 1995 1996 1997 1998 1999 2000 2001

0.025

0.050

0.075 EQ(8)

INFLA Fitted

1994 1995 1996 1997 1998 1999 2000 2001

0.025

0.050

0.075 EQ(9)

INFLA Fitted

Figure 6: Full sample regression with dummy variables

21

6 Testing for linear homogeneity in the rate of in‡ation

Next I want to test the hypothesis that the in‡ation process is linearly homogeneousin the long run. This requires testing for the restriction that the coe¢cients of ¼t¡i(i = 1:::n) and those of ¢4pmt¡k(k = 1:::n) from the dynamic equation sum to one.That is

P8i

®¤i +P8k

°¤k = 1. The F-test based on the retricted regression imposed on

the two forms of the regression, the baseline equation (5), and the equation modi…edwith the dummy variables (9), are shown in Table (5) below.

Table 5: Restricted RegressionRestricted EQ(5) Restricted EQ(9)

(no dummy model) (with dummy)Dependent ¼t ¼tRegressors coe¢cient (t-value) coe¢cient (t-value)constant -0.0016 0.002

¼t¡1 0.961 (140.4) 0.976 (46.8)ygapt¡3 0.024 (1.458) –

D2ygapt¡3 – 0.062 (3.93)D1¢4pm – 0.089 (2.58)

D1¢4pmt¡1 – -0.102 (-2.97)D2¢4pm – 0.037 (6.83)¢4pm 0.039 (5.544) –

R2 0.929 0.952Residual s.e. 0.0062 0.0048

F-test on retriction [p-value] 2.711[0.11] 1.464 [0.24]

Restriction for linear homogeneity is accepted, but now the coe¢cient of laggedin‡ation ¼t¡1 becomes even larger, hence implying an even longer ‘memory’. Forexample, over a 2-year period the e¤ect of an initial shock to output will be (0.96)8 =0:72 multiplied by the output gap coe¢cient. This means that over two years only30% of the shock is dissipated and 70% still remains. Another interesting result isthat under in‡ation homogeneity restriction the e¤ect of output gap increases in themodel with dummy variables. This may point to a merit in applying dummy-variablecontrol in the regression of the Phillips curve in Thailand.

7 Conclusion

The estimation of the Phillips curve in Thailand yields two key …ndings. The …rstis the strong degree of the backward-looking dynamics of the in‡ation process. Thislong ‘memory’ of the curve implies that any policy rule will have a very slow e¤ectwhen bringing down in‡ation back to target, and hence any rule will have to behighly forward-looking. However, this result should be treated with some scepticismbecause the prior speci…cation of the empirical equation of the Phillips curve is purelybackward-looking. Because there is a lack of data in the observations of in‡ationexpectations, and also in other …nancial market variables that can be used as its

22

instruments, there is no scope for a formulation of the regression that allows fora direct e¤ect of in‡ation expectation to show. The identi…cation problem for apartially forward-looking Phillips curve in Thailand is the subject of work in progress.

The second important …nding is that the Phillips curve seems to emerge in Thai-land only at the time of the Asian crisis. This could be a result of two observationallyequivalent hypotheses. First, there may not be an underlying Phillips relationshipin Thailand during the period between the big-bang …nancial liberalisation of theearly 1990s and the Asian crisis, and the Asian crisis itself ‘created’ the Phillips re-lationship. The alternative hypothesis is that the underlying Phillips relationshiphas always existed in Thailand, but because there is no variation in the two drivingvariables in the relationship, the output gap and the import price in‡ation (the latterbecause of the …xed exchange rate regime), the Phillips relationship was not ‘visible’during that period, and only became apparent after the Asian crisis caused largevariations in the two driving variables. Unfortunately, because of the no-variationnature of the …rst half of the data, the two hypotheses cannot really be distinguishedfrom each other. Nevertheless, for the purpose of using the estimated Phillips curvein a small-scale structural model for monetary policy analysis, this distinction is notnecessary.

We also …nd that the coe¢cients of output gap and import price in‡ation are alsosurprisingly small for such a small, open economy. However, they are of a similarsize to those estimated for the UK (Batini and Turnbull (2000)) and for Australia(Gruen, Pagan and Thompson (1999)). The e¤ect of changes in these two variables,and of any supply shock, although initially small, can be magni…ed and sustainedthrough the large autoregressive coe¢cient of the in‡ation term.

References

[1] Ball, L. (1999) “Policy rules for open economies” in Taylor, J. B. (ed) MonetaryPolicy Rules, University of Chicago Press for NBER, Chicago

[2] Batini, N. and A. G. Haldane (1999), “Forward-looking rules for monetary pol-icy” in Taylor, J. B. (ed) Monetary Policy Rules, University of Chicago Press forNBER, Chicago

[3] Batini, N. and K. Turnbull (2000) “Monetary condition indices for the UK : Asurvey”, Bank of England, External MPC Unit, Discussion paper No. 1 (Septem-ber)

[4] Bean, C. R. (1998), “The new UK monetary arrangements”, Economic Journal108, 1795-1809

[5] Calvo, G. A. (1983) “Staggered prices in a utility maximising framework”, Jour-nal of Monetary Economics 12, 383-398

[6] Chant-hom, P. N. Burana-thanang, V. Im-udom, Y. Rathagul and P. Kiatikomol(2001) “Parametric estimation of Thailand’s potential output”, Bank of ThailandSymposium, July 2001.

23

[7] Debelle, G. and J. Vickery (1998), “Is the Phillips curve a curve? Some evidenceand implications for Australia” Economic Record 74(227), 384-398

[8] Fuhrer, J. C. and G. Moore (1995), “In‡ation persistence”, Quarterly Journal ofEconomics 110(1), 127-159

[9] Gali, J. and M. Gertler (1999) “In‡ation dynamics: A structural econometricmodel”, Journal of Monetary Economics 44(2), 195-222

[10] Gruen, D., A. R. Pagan and C. Thompson (1999) “The Phillips curve in Aus-tralia”, Journal of Monetary Economics 44(2), 223-258

[11] Kremers, J. J. M., N. R. Ericsson and J. J. Dolado (1992), “The power of coin-tegration tests”, Oxford Bulletin of Economics and Statistics 54, 159-178

[12] McCallum B. T. and E. Nelson (1999a) “Performance of operational policy rulesin an estimated semiclassical structural model”, in Taylor, J. B. (ed) MonetaryPolicy Rules, University of Chicago Press for NBER, Chicago

[13] McCallum B. T. and E. Nelson (1999b) “Nominal income targeting in an openeconomy optimizing model”, Journal of Monetary Economics 43(3), 553-578

[14] Roberts, J. M. (1995) “New Keynesian Economics and the Phillips curve”, Jour-nal of Money, Credit and Banking 27(4), 975-984

[15] Rotemberg, J. J. and M. Woodford (1999), “Interest rate rules in an estimatedsticky price model”, in Taylor, J. B. (ed) Monetary Policy Rules, University ofChicago Press for NBER, Chicago

[16] Rudd, J. and K. Whelan (2001) “New tests of the New Keynesian Phillips curve”,Federal Reserve Board, Finance and Economics Discussion series, No. 2001-30

[17] Svensson, L. E. O. (1997) “In‡ation forecast targeting: Implementing and mon-itoring in‡ation targets”, European Economic Review 41, 1111-1146

[18] Svensson, L. E. O. (1999) “In‡ation targeting as a monetary policy rule”, NBERWorking Paper No. 6790

[19] Taylor, J. B. (1980) “Aggregate dynamics and staggered contracts”, Journal ofPolitical Economy 88, 1-23

A De…nition of variables and data sources

All data are quarterly observations, started in 1993Q1 and ended in 2000Q4, and arenot seasonally adjusted unless stated otherwise.

Core Consumer Price Index (CPI)Consumer price index at 1994 prices, excluding raw food and energy. (Table 79

Bank of Thailand Databank) The Bank of Thailand’s series is based on a weightedaverage of prices of goods and services regularly consumed. Weights are determined

24

from household spending pattern obtained by surveys implemented by the Depart-ment of Internal Trade, Ministry of Commerce. Core CPI represents 79% of HeadlineConsumer Price Index.

Import Price Index (Pm)Import price index per unit good (Thai baht). (Table 45 Bank of Thailand

Databank) Data used to compile the price index are obtained from the CustomsDepartment. Commodities are grouped into categories according to the standardInternational Trade Classi…cation coding system, with goods in each category classi-…ed according to the 10-digit harmonization system. Prices per unit are calculatedby dividing the value of the goods by their corresponding quantity. The Bank ofThailand currently uses the Fisher Chained Index for the calculation of export andimport price index, with 1995 taken as the base year. A total of 5,000 commoditiesare used to calculate the export price index, while 6,000 commodities are used forthe calculation of import price index, which cover more than 90 percent of the overallexport and import value.

Gross Domestic Product (GDP)Quarterly observations of GDP at 1988 prices (Table 87.1 Bank of Thailand

Databank) are calculated by the National Economic and Social Development Board(NESDB), using raw data obtained from the Ministry of Agricultrue, Ministry ofcommerce, and Ministry of Industry. The series is seasonally adjusted.

Potential Output (y*)The underlying value of national output consistent with a non-accelerating in‡a-

tion rate of unemployment (NAIRU) is constructed from a Cobb-Douglas productionfunction by Chant-hom et al.(2000), used internally by the Monetary Policy Group,Bank of Thailand. In their calculation, the variable used as a proxy for captial in-put K is capital utilisation of the manufacturing sector, and the proxy for labour isactual employment (person) because no man-hour data is available. Both variablesare seasonally unadjusted.

Unit labour cost (ULC)Unit labour costs per person is equal to nominal wages per person divided by

non-agricultural productivity per person.Wages per pserson are sectorally-weighted average monthly wages received by

workers during the reference period in which the biannual Labour Force Survey isconducted. (National Statistics O¢ce) The biannual series was interpolated to formthe quarterly series by ARIMA techniques (Bank of Thailand Monetary Policy Group)

Non-agricultural productivity per person is equal to the ratio of constant pricenon-farm GDP (Table 87.1 Bank of Thailand Databank) to total non-agriculturalemployment (Table 89 Bank of Thailand Databank ).

Fuel price indexImport price index (Thai baht) of mineral, fuels and lubricant (Table 45 Bank of

Thailand Databank)

25