ARTICLE IN PRESS

Resources Policy 34 (2009) 121–132

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Resources Policy

0301-42

doi:10.1

� Corr

E-m

journal homepage: www.elsevier.com/locate/resourpol

Impact of crude oil price volatility on economic activities: An empiricalinvestigation in the Thai economy

Shuddhasawtta Rafiq, Ruhul Salim �, Harry Bloch

School of Economics & Finance, Curtin University of Technology, Perth, WA 6845, Australia

a r t i c l e i n f o

Article history:

Received 15 April 2008

Received in revised form

4 September 2008

Accepted 11 September 2008

JEL Classificaton:

C32

Q43

O13

Keywords:

Oil price volatility

Thailand

Granger causality test

Impulse response function

Variance decomposition

07/$ - see front matter Crown Copyright & 20

016/j.resourpol.2008.09.001

esponding author. Tel.: +618 9266 4577.

ail address: Ruhul.Salim@cbs.curtin.edu.au (R

a b s t r a c t

This paper empirically examines the impact of oil price volatility on key macroeconomic indicators of

Thailand. Following Andersen et al. [2004. Analytical evaluation of volatility forecasts. International

Economic Review 45(4), 1079–1110], quarterly oil price volatility is measured by using the realized

volatility (RV). The impact of the oil price volatility is investigated using the vector auto-regression

(VAR) system. The Granger causality test, impulse response functions, and variance decomposition

show that oil price volatility has significant impact on macroeconomic indicators, such as

unemployment and investment, over the period from 1993Q1 to 2006Q4. Perron’s [1997. Further

evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics 80(2),

355–385] test identifies structural breaks in all the concerned variables during the time of the Asian

Financial Crisis (1997–1998). A VAR for the post-crisis period shows that the impact of oil price volatility

is transmitted to budget deficit. The floating exchange rate regime introduced after the crisis may be the

key contributor to this new channel of impact.

Crown Copyright & 2008 Published by Elsevier Ltd. All rights reserved.

Introduction

Oil, like other primary commodities, is a vital input in theproduction process of an economy. Primary commodity pricesaffect aggregate price levels positively as commodities are used asraw materials in industrial production (Bloch et al., 2006).Similarly, oil is needed to generate electricity, run productionmachinery, and transport the output to the market. Further,volatility in oil prices may reduce aggregate output temporarily asit delays business investment by raising uncertainty or byinducing expensive sectoral resource reallocation (Guo andKliesen, 2005). Although industrialized developed countries seemto be more dependent on oil, evidence shows that the demand foroil in developing countries is on an increasing trend (Birol, 2007).

Most of the earlier studies concerning oil price shocks orvolatility and economic activities have been conducted in thecontext of developed economies; for example Hamilton (1983),Burbridge and Harrison (1984), Gisser and Goodwin (1986), Mory(1993), Mork and Olsen (1994), and Ferderer (1996), amongothers. Research concerning the impact of oil price volatility in the

08 Published by Elsevier Ltd. All

. Salim).

context of developing countries is very limited. This is partly dueto the lack of reliable data and partly due to the less historicaldependence of these countries on oil. However, since thesecountries are presently experiencing increased demand forenergy, a through investigation of the impact of oil pricevariability on these economies is warranted. This paper aims toanalyze the impact of oil price volatility on different macro-economic variables of Thailand, such as output, price level,unemployment, interest rate, fiscal deficit, investment, and tradebalance.

Thailand serves as a suitable case for four main reasons. Firstly,the Thai economy has a convincing track record of growth throughprivatization and widespread trade liberalization compared withother developing economies. Secondly, since Thailand has limiteddomestic oil production and reserves, and imports make up asignificant portion of the country’s oil consumption; the Thaieconomy seems to be relatively more vulnerable to oil pricechanges compared with other developing countries with greateroil supply security. Thirdly, there exists no prior study of this typefor Thailand. Finally, the accuracy and availability of the relevantdata play an important role in choosing Thailand for study.

The remainder of the paper is organized as follows. Macro-economic implications of oil price volatility offers two differentchannels through which oil price volatility may impact the

rights reserved.

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S. Rafiq et al. / Resources Policy 34 (2009) 121–132122

macroeconomy. Oil price and the economy presents a criticalreview of earlier literature followed by an analytical framework inData sources and analytical framework. Empirical results from theestimation are presented in Analysis of findings. Conclusion andpolicy implications are offered in the final section.

Macroeconomic implications of oil price volatility

It is now well established in both empirical and theoreticalliterature that oil price shocks exert adverse impacts on differentmacroeconomic indicators through raising production and opera-tional costs. Alternatively, large oil price changes—either in-creases or decreases, i. e. volatility—may affect the economyadversely because they delay business investment by raisinguncertainty or by inducing costly sectoral resource reallocation.

Bernanke (1983) offers theoretical explanation of the uncer-tainty channel by demonstrating that, when the firms experienceincreased uncertainty about the future price of oil then it isoptimal for them to postpone irreversible investment expendi-tures. When a firm is confronted with a choice of whether to addenergy-efficient or energy-inefficient capital, increased uncer-tainty born by oil price volatility raises the option value associatedwith waiting to invest. As the firm waits for more updatedinformation, it forgoes returns obtained by making an earlycommitment, but the chances of making the right investmentdecision increase. Thus, as the level of oil price volatility increases,the option value rises and the incentive to investment declines(Ferderer, 1996). The downward trend in investment incentivesultimately transmits to different sectors of the economy.

Hamilton (1988) discusses the sectoral resource allocationchannel. In this study by constructing a multi-sector model, theauthor demonstrates that relative price shocks can lead to areduction in aggregate employment by inducing workers of theadversely affected sectors to remain unemployed while waitingfor the conditions to improve in their own sector rather thanmoving to other positively affected sectors. Lilien (1982) extendsHamilton’s work further by showing that aggregate unemploy-ment rises when relative price shocks becomes more variable.

Oil price and the economy

Oil price changes impact real economic activities on both thesupply and demand side (Jimenez-Rodriguez and Sanchez, 2005).The increase in oil price is reflected in a higher production costthat exerts adverse effects on supply. The higher production costlowers the rate of return on investment, which affects investmentdemand negatively. Besides, increased volatility in oil price mayaffect investment by increasing uncertainty about future pricemovements. Consumption demand is also influenced by thechanges in oil price as it affects product price by changingproduction cost. Moreover, a rise in oil prices deteriorates theterms of trade for oil-importing countries (Dohner, 1981). As oil isdirectly linked to the production process, it can have a significantimpact on inflation, employment, and output. An oil price shockcan increase inflation by increasing the cost of production. It alsoaffects employment, as inflationary pressure may lead to a fall indemand and this, in turn, leads to a cut in production, which cancreate unemployment (Loungani, 1986). The employment–oilprice relationship holds true for not only industrial production,it is equally true for agricultural employment (Uri, 1995).

Previous research in this field mainly investigates two differentaspects of the relationship between oil price and economicactivities: the impact of oil price shock and the impact of oilprice volatility. These two approaches differ in the way they

incorporate oil price in their model. While the first approach takesoil prices at their levels, the second approach employs differentvolatility measures to capture the oil price uncertainty.

In response to two consecutive oil shocks in the early and late1970s, a considerable number of studies have examined theimpact of shocks to oil price levels on economic activities.Pioneering work by Hamilton in the early 1980s on the relation-ship between oil price and economic activities spurred research-ers to look into the issue in greater detail. Hamilton (1983)analyzes the behavior of oil price and the output of the USeconomy over the period 1948–1981, and concludes that every USrecession between the end of World War II and 1973 (except the1960–1961 recession) has been preceded, with a lag of aroundthree-fourths of a year, by a dramatic increase in the price of crudepetroleum. He further notes that post-1972 recessions in the USwere mainly caused by OPEC’s supply-oriented approach. In hissubsequent works, Hamilton (1988, 1996) strengthens his convic-tion that there is an important correlation between oil shocks andrecession. In a recent survey, Hamilton (2008) further stresses theimportance of oil price on macroeconomic activities.

Since then a number of researchers have supported andextended Hamilton’s results. Mork (1989) examines the relationbetween oil price change and GNP growth in the US with anextended data set (1948–1988) to capture the effect of bothupward and downward movements of oil price on output.Hamilton considers only large upward price movements and findsthat there is a significant negative correlation between oil priceand output. The major contribution of Mork’s study is that it findsan asymmetric impact of oil prices on economic activities.Burbridge and Harrison (1984), using somewhat different meth-ods and OECD data, find mixed but overall reinforcing evidence ofimpact based on analysis of the US, Japan, Germany, the UK, andCanada. They find that oil price increases have a sizeable negativeimpact on industrial production in the US and the UK, but theresponses in other countries are small. This study also findsnegative relationships between the oil price shock and macro-economic indicators by using a comprehensive empirical model.

Gisser and Goodwin (1986) work on the US economy coveringthe period 1961Q1–1982Q4. They employ a reduced-formapproach to assess the quantitative significance of the impact ofcrude oil prices on the US economy. They find that crude oil priceshave a significant impact on output, even exceeding the impacts ofmonetary and fiscal policies. Mork and Olsen (1994) examine thecorrelation between oil price and GDP in seven OECD countries(the USA, Canada, Japan, West Germany, France, the UK, andNorway) over the period of 1967Q3–1992Q4. They find asignificant negative correlation between oil price increases andGDP in most of the countries studied. They estimate bi-variatecorrelations as well as partial correlations within a reduced-formmacroeconomic model. The correlations between oil priceincreases and GDP are found to be negative and significant formost of the countries, but positive for Norway, whose oil-producing sector is large relative to the economy as a whole.The correlations with oil price decreases are mostly positive, butsignificant only for the USA and Canada.

Cunado and Gracia (2005) examine the impact of oil priceshocks on economic activities and inflation in six Asian countries,namely Japan, Singapore, South Korea, Malaysia, Thailand, and thePhilippines. Using quarterly data from 1975Q1 to 2000Q2 theyfind that oil prices have a significant impact on both economicgrowth and inflation, and this result is more significant when oilprice is measured in local currencies. They also find evidence ofthe asymmetric effect of oil prices on economic activities in theirstudy. Although the paper is a brilliant attempt to study Asianeconomies, which are not given that much attention in previousworks in this field, it could have considered other indicators of

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aggregate macroeconomic performance, such as unemployment,interest rates, or exchange rates.

Cologni and Manera (2008) investigate the impact of oil priceson inflation and interest rates in a co-integrated vector-auto-regreassive (VAR) framework for G-7 countries. Using quarterlydata for the period 1980Q1–2003Q4, they find that, except forJapan and the UK, oil prices significantly affect inflation, which istransmitted to the real economy by increasing interest rates.Impulse response function analysis suggests the existence of aninstantaneous, temporary effect of oil price change on inflation.

Chen and Chen (2007) examine whether there is any long-runequilibrium relation between real oil prices and real exchangerates. Using the monthly panel data of G7 countries over theperiod 1972M1–2005M10 they find a co-integrating relationshipbetween real oil prices and real exchange rates. This paper isdifferent from other studies in this field (for example Zhou, 1995;Chaudhuri and Daniel, 1998; Amano and Norden, 1998) in that itassesses the role of real oil prices in predicting real exchange ratesover long horizons. Panel predictive regression estimates suggestthat real oil prices have significant forecasting power for realexchange rates.

Lardic and Mignon (2006) study the long-run equilibriumrelationship between oil prices and GDP in 12 European countriesusing quarterly data spanning from 1970Q1 to 2003Q4. This studyfinds that the relationship between oil price and economicactivities is asymmetric; that is, rising oil prices retard aggregateeconomic activity more than falling oil prices stimulate it. Theirresults show that, while the standard co-integration between thevariables is rejected, there is asymmetric co-integration betweenoil prices and GDP in most of the participating Europeancountries. This paper makes a significant contribution to theliterature on the asymmetric impact of oil price on GDP and itdiffers from other studies, such as Mory (1993), in that it employsan asymmetric co-integration procedure to capture this asym-metric relation.

In contrast to the above studies, which analyze the impact ofoil price shocks, papers investigating the impact of oil pricevolatility on the economies are very limited and have their originin the increase of oil price volatility from mid-1980s. Lee and Ni(1995) find that oil price changes have a substantial impact oneconomic activities (notably GNP and unemployment) onlywhen prices are relatively stable, rather than highly volatile orerratic. This indicates a weaker empirical relationship betweenoil prices and economic activities in the US since the remarkableincrease in oil price volatility. In this study the authors utilizea generalized auto-regressive conditional heteroskedasticity(GARCH) model to construct the conditional variation in oilprice changes.

Ferderer (1996) analyzes US data spanning from 1970M1 to1990M12 to see whether the relation between oil price volatilityand macroeconomic performance is significant. In this study, theoil price volatility is measured by simple standard deviation. Oilprice volatility is found to contain significant independentinformation that helps forecast industrial production growth.The vector auto-regressive (VAR) framework is utilized to analyzethe impact of both oil price shock and oil price volatility on fiscaland monetary policy variables like industrial production growth,federal funds rate and, non-borrowed reserves. Evidence is foundthat oil market disruptions have impact in the economy throughboth sectoral shocks and uncertainty channels. Further, monetarytightening in response to oil price increase partially explains theoutput–oil price correlation and the Federal Reserve reacts to theoil price increase as much as it responds to the oil price decreases.The paper concludes that sectoral shocks and uncertaintychannels offer a partial solution to the asymmetry puzzle betweenoil price and output.

Guo and Kliesen (2005) look into the impact of oil pricevolatility on the US economy. Using the measure of realizedvolatility constructed from daily crude oil future prices traded onthe NYMEX, they find that, over the period 1984–2004, oil pricevolatility has a significant effect on various key US macroeco-nomic indicators, such as fixed investment, consumption, employ-ment, and the unemployment rate. The findings suggest thatchanges in oil prices are less significant than the uncertaintyabout future prices. They also find that standard macroeconomicvariables do not forecast realized oil price volatility, whichindicates that the variance of future oil prices reflect stochasticdisturbances. They conclude that this is mainly driven byexogenous events, like significant terrorist attacks and militaryconflicts in the Middle East.

Some observations can be made from the above discussion.Firstly, oil price shocks have important impact on aggregatemacroeconomic indicators, such as GDP, interest rates, invest-ment, inflation, unemployment and exchange rates. Secondly, theimpact of oil price changes on the economy is asymmetric; that is,the negative impact of oil price increases is larger than thepositive impact of oil price decreases. There have been fewacademic endeavors made to analyze the impact of oil pricevolatility per se on economic activities and, more importantly,such studies are conducted almost exclusively in the context ofdeveloped countries, especially the USA.

Studies analyzing the impact of oil price volatility arenow needed for developing countries. In the face of globalcompetition, maintaining economic stability has become thecrucial task for policy makers in these countries. Moreover,the economies of developing countries are fundamentallydifferent from those of developed countries. Developing countriesare generally characterized by relatively high unemployment,less-developed financial markets, weak infrastructure, etc.Moreover, the dependence of developing countries on oil isforecasted to be increasing over the next couple of decades. Thus,it is of utmost importance to identify what impact oil pricevolatility has on the economic activities of these countries. As noknown studies have examined this aspect in the context ofdeveloping countries, this remains an untapped area of seriousresearch. This study intends to make some contribution to anunderstanding of the issue of oil price volatility and its impacton the real economic activities of one of these developingcountries, Thailand.

Data sources and analytical framework

Data

This paper uses quarterly data from 1993Q1 to 2006Q4 forThailand. The rationale behind selecting this period is theavailability of data. Since the volatility of oil price is calculatedon a quarterly basis, the empirical analysis of this study is basedon quarterly data. In Thailand, there is no data of all the relevantmacroeconomic indicators prior to 1993. Thus this study has toconfine its empirical analysis within this range of time. Incalculating the quarterly volatility measure, the daily crude oilprices of ‘‘Arab Gulf Dubai FOB $US/BBL’’ are considered andtransformed into local prices by adjusting the world oil priceswith the respective foreign exchange rates. Time-series data onsome macroeconomic variables, growth rate of gross domesticproduct (GGDP), investment (INV), interest rate (IR), inflation(INF), are obtained from the International Financial Statistics’ (IFS)compact disk (CD) of May 2007 and data on unemployment rates(UR), trade balance (TB), and budget deficit (BD) are collectedfrom the Bank of Thailand. Investment is calculated by the gross

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S. Rafiq et al. / Resources Policy 34 (2009) 121–132124

fixed capital formation as a percentage of GDP. Both the grossfixed capital formation and real GDP data are on national currencyin millions. The budget deficit is also taken as a percentage of GPDto measure its share of overall economic activity to capture theimpact of subsidization through the oil fund that is operated bythe Energy Fund Administration Institute (EFAI) of Thailand, whiletrade balance is computed by subtracting net exports from netimports and also taken as a percentage of GDP. The interest ratedata represents 3-month Treasury bill rate. In the case ofunemployment rates, quarterly observations from 1993Q1 to2000Q4 are obtained by using the Lisman Sandee (1964) method.The measure applied here in constructing quarterly oil pricevariance is the measure of realized volatility, which is elaboratedbelow.

Realized oil price variance

Based on the nature of data under consideration, variousvolatility measures, both parametric and non-parametric (such ashistorical volatility (HS), stochastic volatility (SV), impliedvolatility (IV), realized volatility (RV), and conditional volatility(CV)) have been suggested in the literature. The parametricmodels can reveal well-documented time varying and clusteringfeatures of conditional and implied volatility. However, thevalidity of the estimate relies a great deal on the modelspecifications along with the particular distributional assump-tions and, in the instances of implied volatility, another assump-tion regarding the market price of volatility risk has to be met(Andersen et al., 2001a). This stylized fact is also unveiled in aseminal article by Andersen et al. (2001b), where they argue thatthe existence of multiple competing parametric models points outthe problem of misspecification. Moreover, the conditionalvolatility (CV) and stochastic volatility (SV) models are hard toadopt in a multivariate framework for most of the practicalapplications.

An alternative measure of volatility, termed as realized

volatility, is introduced by Andersen et al. (2001a) and Andersenet al. (2001b, 2003). Furthermore, the theory of quadraticvariation suggests that, under appropriate conditions, realizedvolatility is an unbiased and highly efficient estimator of volatilityof returns, as shown in Andersen et al. (2001b, 2003), andBarndorff-Nielsen and Shephard (2002, 2001a). In addition to that,by treating volatility as observed rather than latent, the approachfacilitates modeling and forecasting using simple methods basedon observable data (Andersen et al., 2003).

According to Andersen et al. (2004), realized volatility is thesummation of intra-period squared returns

RVtðhÞ �X1=h

i¼1

rðhÞ2t�1þih

where the h-period return (in this study this is daily oil pricereturn) is given by rðhÞt ¼ logðStÞ � logðSt�hÞ and 1/h is a positiveinteger. In accordance with the theory of quadratic variation, therealized volatility RVt(h) converges uniformly in probability to IVt

as h-0, as such allowing for ever more accurate non-parametricmeasurements of integrated volatility. Furthermore, papers ofZhang et al. (2005) and Aıt-Sahalia et al. (2005) state that therealized variance is a consistent and asymptotically normalestimator once suitable scaling is performed. The estimatedrealized volatility and all macro-variables used in this study aregraphically represented below. These figures reveal two importantfacts; (i) crude oil price has been highly volatile in recent years,particularly in the second half of 1990s and (ii) all the data seriesportray spikes around the period of the Asian Financial Crisis of1997–1998.

Methodology

This article employs the Granger causality test to examine thecausal relationship between oil price volatility and other leadingeconomic indicators of Thailand. Causality in Granger’s (1969)sense is inferred when values of a variable, say, Xt has explanatorypower in a regression of Yt on lagged values of Yt and Xt. If laggedvalues of Xt have no explanatory power for any of the othervariables in the system, then Yt is viewed as weakly exogenous tothe system.

Vector auto-regression (VAR) of the following form is consi-dered for this purpose:

Yt ¼ a0 þXn

i¼1

biYt�i þXn

i¼1

liXt�i þ mt (1)

Xt ¼ f0 þXn

i¼1

jiYt�i þXn

i¼1

ZiXt�i þ nt (2)

where n is the number of the optimum lag length. Optimum laglengths are determined empirically by the Schwarz InformationCriterion (SIC). For each equation in the above VAR, Wald w2

statistics are used to test the joint significance of each of the otherlagged endogenous variables in equation. In addition, the Wald w2

statistics tell us whether an endogenous variable can be treated asexogenous. Moreover, roots of the characteristics polynomial testis undertaken to confirm whether the VAR system satisfies thestability condition (Fig. 1).

The conventional Granger causality test based on standard VARis conditional on the assumption of stationarity of the variablesconstituting the VAR. If the time series are non-stationary, thestability condition of VAR is not met, implying that the w2 (Wald)test statistic for Granger causality is invalid. In that case, the co-integration and vector error correction model (VECM) arerecommended to investigate the relationship between non-stationary variables. Therefore, it is imperative to ensure firstthat the underlying data are stationary or I(0). Since there are anumber of tests developed in the time-series econometrics to testthe presence of unit roots, this paper uses two most popularmethods: the Augmented Dickey–Fuller (ADF) test and thePhilips-Perron (PP) test. However, in many empirical literatureit has been found that both the ADF and PP unit root tests fail toreject null hypothesis of a unit root for many time series.Therefore, the study also employs the Kwiatkowaski–Philips–Schmidt–Shin (KPSS) unit root test to complement the standardunit root testing process since KPSS can make distinction betweenthe series that appear to be stationary, those that appear to benon-stationary having a unit root at their levels, and those thatare not sufficiently informative to be certain whether theyare either of them. Hence, the combining use of these testsmakes it possible to test for both the null hypothesis of non-stationarity and stationarity, respectively. This process of joint useof unit root (ADF and PP) and stationarity (KPSS) tests is known asconfirmatory data analysis (Brooks, 2002).

However, these standard tests may not be appropriate whenthe series contain a structural break. To compensate for such aproblem, Perron (1989) proposes a procedure that allows anexogenous structural break at time Tb, that is if the time of break isknown as a priori. Afterward, Zivot and Andrews (1992) criticizethis test for treating the time of the break as exogenous or a priori.Zivot and Andrews (1992), and latter Perron (1997), furtherdevelop a procedure that allows endogenous break points in theseries under consideration. Following this, a large number ofempirical studies have considered the probable existence ofstructural break(s) in the series under consideration, such as

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Realized Volatility (RV)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

1994 1996 1998 2000 2002 2004 2006-10.0

-7.5

-5.0

-2.5

0.0

2.5

5.0

7.5

10.0

1994 1996 1998 2000 2002 2004 20060.6

0.8

1.0

1.2

1.4

1.6

1.8

1994 1996 1998 2000 2002 2004 2006

Unemployment Rate (UR)

0

1

2

3

4

5

1994 1996 1998 2000 2002 2004 2006-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1994 1996 1998 2000 2002 2004 20062

4

6

8

10

12

14

1994 1996 1998 2000 2002 2004 2006

Trade Balance (TB)

-15

-10

-5

0

5

10

15

1994 1996 1998 2000 2002 2004 2006-12

-8

-4

0

4

8

1994 1996 1998 2000 2002 2004 2006

GDP Growth (GGDP) Investment (INV)

Inflation (INF) Interest Rate (IR)

Budget Deficit (BD)

Fig. 1. Variables used in this study.

S. Rafiq et al. / Resources Policy 34 (2009) 121–132 125

Salman and Shukur (2004), Hacker and Hatemi-J (2005), andSalim and Bloch (2007), among others.

Breaks in time-series data due to a shock occur eitherinstantaneously or gradually. Instantaneous change to the newtrend function is modeled in the Additive Outlier (AO) modeland changes that take place gradually are modeled in theInnovational Outlier (IO) model. In the present context it isreasonable to follow the IO model, because policy reforms atmacro level do not cause the target variable to respondinstantaneously to the policy actions. Also the date of break isnot known a priori. Therefore, an IO model, following Perron(1997), is used to test the stationary process with a structuralbreak both in slope and intercept

yt ¼ mþ bt þ yDUt þ gDTt þ dDðTbÞt þ ayt�1

þXk

i

aiDyt�i þ et (3)

where DUt is an indicator dummy variable for a mean shiftoccurring at each possible breakpoint (Tb) while DT�t is a

corresponding trend shift variable; formally

DUt ¼1; if t4Tb

0; otherwise

(DTt ¼

t � Tb if t4Tb

0; otherwise

(

DTb ¼1; if t ¼ Tb þ 1

0; otherwise

(

and yt is any general ARMA process and et a white noise errorterm.

The null hypothesis that a given series is a realization of atime-series process characterized by the presence of a unit root isrejected when the absolute value of the t-statistics for testinga ¼ 1 in (3), denoted by ta, is greater than the critical values. Thebreakpoint is estimated by the OLS for t ¼ 2,y, T�1, thus T�2regressions are run, and the breakpoint is determined by theminimum t statistic on the coefficient of the auto-regressivevariable (ta). The truncation lag parameter k is determined usingthe data-dependent method proposed by Perron (1997). Theoptimum k(k*)is selected such that the coefficient on the last lagin an auto-regression of order k* is significant and that the last

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Table 1Perron innovational outlier model with change in both intercept and slope

Series T Tb k1tb ty tg

td a ta Inference

RV 19 1997Q4 0 1.45 3.26 �2.43 6.14 0.183 �7.56 S

GGDP 20 1998Q1 5 �4.98 �3.55 4.73 �5.46 �0.48 �5.39 S

INV 17 1997Q2 0 0.79 �6.80 2.02 4.96 0.54 �6.65 S

UR 19 1997Q4 1 �1.59 6.35 �1.23 �2.56 �0.09 �7.78 S

INF 21 1998Q2 1 2.38 �3.14 �0.77 1.74 �0.18 �6.90 S

IR 22 1998Q3 0 4.48 �7.56 �1.89 6.76 0.58 �8.79 S

TB 17 1997Q2 8 0.93 3.67 �1.51 �3.01 �0.01 �6.031 S

BD 20 1998Q1 7 �2.69 �4.07 3.29 1.92 �1.06 �4.35 NS

DBD 18 1997Q3 1 �1.85 �1.68 2.06 1.42 �1.07 �14.55 S

Note: 1%, 5%, and 10% critical values are �6.32, �5.59, and �5.29, respectively (Perron, 1997). The optimal lag length is determined by AIC with kmax ¼ 8. S and NS stand for

stationary and non-stationary, respectively.

S. Rafiq et al. / Resources Policy 34 (2009) 121–132126

coefficient in an auto-regression of order greater than k* isinsignificant, up to a maximum order kmax (Perron, 1997).Following Lumsdaine and Papell (1997), and Pahlavani (2005), itis assumed that kmax ¼ 8.

Impulse response functions trace the responsiveness of thedependent variable in the VAR system to a unit shock in errorterms. For each variable from each equation, a unit shock isapplied to the error term and the effects upon the VAR over timeare noted. If there are g variables in the VAR system, then a total ofg2 impulse responses could be generated. However, in accordancewith the objective of this paper, the responses generated by theinnovation in the realized volatility of oil prices are examined.

One limitation with the Granger causality test is that theresults are valid within the sample, which is useful in detectingexogeneity or endogeneity of the dependent variable in thesample period, but is unable to deduce the degree of exogeneity ofthe variables beyond the sample period. To examine this issue thevariance decomposition technique is employed. Unlike impulseresponses, a shock to the ith variable not only directly affects theith variable, but it also transmitted to all of the other endogenousvariables through the dynamic (lag) structure of the VAR. Variancedecomposition separates the variation in an endogenous variableinto the component shocks to the VAR. Thus, variance decom-position provides information about the relative importance ofeach random innovation affecting the variables in the VAR. Sims(1980) notes that, if a variable is truly exogenous with respect toother variables in the system, own innovations will explain all ofthe variables forecast error variance.

From empirical point of view, this study differs from theprevious ones in several ways. One, it applies a comparatively newmeasure of oil price volatility namely the realized oil pricevariance. Two, it performs a battery of diagnostic tests foridentifying unit roots within different time series. Some of thesetests have not previously been employed in similar studies;Ng–Perron, DF-GLS and Perron (1997) unit root tests for example.This study remains one of the very few studies in this field as itemploys impulse responses and variance decompositions testsbased on the results of structural break test.

Analysis of findings

Time-series properties of data

The tests for unit root are applied to both the original seriesand to the first differences.1 Findings of ADF and PP tests differ intheir results, while KPSS reveals that all the variables arestationary at their levels. However, since these conventional unit

1 Results not reported, will be provided upon request.

root tests may suffer from low power the study further employstwo other tests, namely Ng–Perron and Dickey–Fuller GLS (ERS)tests. The results for these tests also confirm that most of thevariables are stationary at their levels. Nevertheless, the signifi-cant part of all these tests is that they all indicate that realizedvolatility (RV) is I (0).

However, as mentioned earlier, the traditional unit root testcannot be relied upon if the underlying series contains structuralbreak(s). This study uses Perron’s (1997) unit root test, whichallows for a structural break and the test results are summarizedin Table 1. The test results provide a very interesting fact that forall the variables the dates for structural break are around theAsian Financial Crisis of 1997–1998. Thus, the study employsmultiple VAR systems: firstly, a VAR analysis for the whole sampleand, secondly, a VAR analysis for the data period after a datebeyond the structural breaks, i.e. from 1999Q1 to 2006Q4.

The Perron (1997) test results provide evidence of theexistence of unit root in the budget deficit when breaks areallowed. However, when the series is differenced once, thevariable becomes stationary. Thus, it can be concluded that theunderlying data is non-stationary at level but stationary at its firstdifference. However, the volatility of oil prices is stationary atlevel. Since there is one non-stationary variable at level the errorcorrection model cannot be implemented in this regard. Hence,the impact analysis needs to be performed in the VAR systemwhere the only non-stationary variable i.e. budget deficit, is firstdifferenced before putting it into the model. Now the concern iswhether an unrestricted VAR or a restricted one (to be morespecific VARX) is appropriate for this particular study.

Model selection

A number of tests are performed to identify the appropriatemodel for investigating the relationship among the variables underconsideration. In selecting the correct form of the model, the firststep is to perform the VAR Granger causality/block exogeneityWald Test. This test investigates whether an endogenous variablecan be treated as exogenous. The result of the exogeniety test forrealized volatility is presented in Table 2. The statistic in the lastrow (all) is the w2 statistic for joint significance of all other laggedendogenous variables in the equation, and the table reveals that, inrespect of all other variables, realized volatility of oil prices can betreated as an endogenous variable in the model.

Moreover, the same test can be employed to identify whethervolatility in oil prices Granger causes other variables in thesystem. The results of the exogeneity test for the other variablesare given in Table 3. The VAR Granger causality test indicatesthat GDP growth, investment, unemployment, and inflation areGranger caused by oil price volatility.

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Lag length selection, stability test, and VAR estimation

According to the SIC, the lag length of VAR is identified to be 1.Lag exclusion tests are also carried out for lag 1. The w2 (Wald)

Table 3Granger causality test

Dependent variable w2 D.F. Probability

GGDP 9.365603 1 0.0022

INV 4.831090 1 0.0280

UR 15.60052 1 0.0001

INF 3.700295 1 0.0544

IR 0.455655 1 0.4997

TB 0.019409 1 0.8892

DBD 0.384085 1 0.5354

Note: Here RV is excluded from the equations for other variables.

Table 4VAR (1) output for Thailand

RV GGDP INV UR

RV(�1) 0.016039 �100.7969 �0.340719 13.8296

(0.15666) (32.9366) (0.80765) (3.5014

GGDP(�1) 1.260000 �0.233898 0.002400 0.0466

(0.00068) (0.14325) (0.00351) (0.0152

INV(�1) �0.032923 �8.112659 0.764016 �0.8407

(0.02212) (4.64947) (0.11401) (0.4942

UR(�1) �0.006491 �1.470879 �0.015807 0.4881

(0.00439) (0.92298) (0.02263) (0.0981

INF(�1) 0.004181 0.147161 �0.029002 0.1519

(0.00377) (0.79335) (0.01945) (0.0843

IR(�1) 0.001620 �0.090718 �0.004775 �0.0244

(0.00084) (0.17710) (0.00434) (0.0188

TB(�1) 0.001501 �0.034675 �0.008827 0.0153

(0.00078) (0.16377) (0.00402) (0.0174

DBD(�1) 5.540000 �0.555279 0.004277 �0.0210

(0.00070) (0.14709) (0.00361) (0.0156

C 0.071668 18.15349 0.404566 1.6867

(0.03399) (7.14526) (0.17521) (0.7595

Notes: Standard errors are reported in ( ).

Table 2Test of endogeniety for RV

Excluded variable w2 D.F. Probability

GGDP 0.000340 1 0.9853

INV 2.216266 1 0.0136

UR 2.186352 1 0.0139

INF 1.227465 1 0.2679

IR 3.698375 1 0.0545

TB 3.712326 1 0.0540

DBD 0.006277 1 0.9368

All 20.51950 0.0046

Note: Here RV is dependent variable.

statistics for the significance of all the endogenous variables at lag1 for each equation separately and jointly reveals that the w2 teststatistics for lag exclusion is significant, e.g. according to the testthe lag length of 1 appears to be appropriate for the VAR systemunder consideration.

The inverse roots of the characteristics auto-regressive (AR)polynomial indicates that the estimated VAR is stable (stationary)if all roots have a modulus less than one and lie inside the unitcircle. Since no root lies outside the unit circle and all the modulusare less than one, the VAR model in this regard is stable. Thus,results from all the tests demonstrate that a VAR (1) is appropriatefor investigating the relationship between volatility of oil pricesand other concerned macroeconomic indicators.

The result of the VAR (1) model is presented in Table 4. Thecoefficients and respective t-statistics of the variables show thatthe realized volatility of oil prices has a significant negativeimpact on GGDP and unemployment. Thus, it shows that, inThailand, oil price volatility negatively affects to GDP growth andit also gives rise to unemployment. Now a critical analysisconcerning the sign of relationships and how long it would takefor the impact of the volatility to work through the system iswarranted.

Source of variability

The Granger causality test suggests which of the variables inthe models have statistically significant impacts on the futurevalues of each of the variables in the system (Brooks, 2002).However, the result will not, by construction, be able to explainthe sign of the relationship or how long these impacts will remaineffective in the future. Variance decomposition and impulse

INF IR TB DBD

4 12.41577 4.568806 �3.974950 22.00455

0) (6.45440) (6.76838) (28.5316) (35.5058)

67 �0.013307 �0.051741 �0.273436 0.296684

3) (0.02807) (0.02944) (0.12409) (0.15442)

08 0.517018 �8.080000 �5.424502 7.892113

7) (0.91113) (0.95545) (4.02764) (5.01213)

16 �0.175170 �0.274910 �0.585587 1.483793

2) (0.18087) (0.18967) (0.79953) (0.99497)

61 0.187564 0.441827 1.362374 �2.152068

4) (0.15547) (0.16303) (0.68725) (0.85523)

87 0.015629 0.850663 �0.054799 0.237514

3) (0.03471) (0.03639) (0.15342) (0.19092)

12 �0.042119 �0.042678 0.785600 0.075512

1) (0.03209) (0.03365) (0.14187) (0.17654)

44 0.037688 �0.064336 �0.046624 0.051830

4) (0.02883) (0.03023) (0.12742) (0.15857)

56 �0.074393 1.014942 7.802119 �13.83383

9) (1.40021) (1.46833) (6.18963) (7.70260)

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response functions give this information. In relation to these tests,it is often observed that the results of these tests are sensitive tothe ordering of the variables. However, with respect to the studyin hand no major difference is seen in changing the order ofpresentation of the concerned variables. The ordering used forboth of the tests, i.e. impulse response functions and variancedecomposition is: realized volatility, GDP growth, investment,unemployment rate, inflation, interest rate, trade balance, andbudget deficit.

Impulse response functions

The orthogonalized impulse response functions trace outresponsiveness of the dependent variables in the VAR to shocksto each of the variables. For each variable from each equationseparately, a unit shock is applied to the error, and the effectsupon the VAR system over time are noted. Since the VAR systemhas eight variables, a total of 64 impulses could be generated.Since the primary aim of the paper is to examine the impact of oilprice volatility on the other seven economic indicators, the paperonly traces out the responsiveness of the dependent macroeco-nomic variables in the VAR to shock to realized volatility. The

-0.005

0.000

0.005

0.010

0.015

0.020

5 10 15 20 25 30 35 40

Response of RV to RV

-3

-2

-1

0

1

2

5 10 15 2

Response of

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

5 10 15 20 25 30 35 40

Response of UR to RV

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

5 10 15 2

Response o

-1

0

1

2

3

5 10 15 20 25 30 35 40

Response of TB to RV

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

5 10 15 2

Response o

Fig. 2. Responses from RV, GGDP, INVT, UR, INF, IR, TB a

results of the impulse responses of the variables are presentedin Fig. 2.

According to the above figures, the impulse responses showthat, in most of the cases, realized volatility has its impact on theshorter time horizon and the highest impact would happen toinvestment and unemployment rate.

Variance decomposition

Variance decomposition gives the proportion of the move-ments in the dependent variables that are due to their ‘‘own’’shocks, versus shocks to the other variables. The results ofvariance decomposition over a period of a 40-quarter timehorizon for different variables are presented in Table 5. As thetable below suggests, the variance decomposition results areconsistent with the findings of impulse responses. The results ofthe test reveal that oil price volatility explains a fair portion ofinnovations in investment and unemployment rate over a longertime period horizon.

However, since the Perron (1997) test for structural breakindicates the existence of structural break for all the variables inthe system, the relationship is further investigated in the vector

0 25 30 35 40

GGDP to RV

-0.12

-0.08

-0.04

0.00

0.04

5 10 15 20 25 30 35 40

Response of INV to RV

0 25 30 35 40

f INF to RV

-1.2

-0.8

-0.4

0.0

0.4

0.8

5 10 15 20 25 30 35 40

Response of IR to RV

0 25 30 35 40

f DBD to RV

nd DBD to Cholesky One S.D. Innovations+S.E in RV.

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Table 6VAR granger causality/block exogeneity wald tests for RV

Excluded variable w2 D.F. Probability

GGDP 7.878130 2 0.0195

INV 0.544623 2 0.7616

UR 9.290095 2 0.0096

INF 1.514428 2 0.4690

IR 0.169897 2 0.9186

TB 1.363557 2 0.5057

BD 0.256577 2 0.8796

All 20.51950 0.0195

Note: Here RV is dependent variable.

Table 7VAR granger causality/block exogeneity Wald tests for other variables

Dependent variable w2 D.F. Probability

GGDP 3.702518 2 0.1570

INV 5.038456 2 0.0805

UR 0.357929 2 0.8361

INF 2.367984 2 0.3061

IR 1.547978 2 0.4612

TB 2.659912 2 0.2645

BD 3.989715 2 0.1360

Note: Here RV is excluded from the equations for other variables.

Table 5Bivariate variance decomposition of RV with GGDP, INV, UR, INF, IR, DTB, and DBD

Period S. E. RV GGDP S.E. RV INV S.E. RV UR S.E. RV INF

1 3.428 0.318 99.682 0.084 8.269 91.512 0.364 3.472 85.474 0.672 1.887 74.742

10 4.583 15.131 64.230 0.266 23.222 34.498 0.941 26.196 17.374 0.882 11.440 49.466

20 4.602 15.165 63.715 0.281 22.514 31.919 0.987 25.559 15.811 0.922 12.414 47.053

30 4.608 15.183 63.606 0.289 22.389 31.500 1.004 25.335 15.286 0.925 12.411 46.76536

40 4.608 15.182 63.594 0.290 22.387 31.289 1.008 25.313 15.157 0.927 12.463 46.618

Period S.E. RV IR S.E. RV TB S.E. RV DBD

1 0.704 1.619 86.778 2.969 5.178 82.905 3.696 0.917 46.519

10 2.500 8.123 30.293 6.611 15.597 46.778 4.427 3.152 34.972

20 3.491 14.692 16.085 6.766 15.621 45.314 4.428 3.159 34.954

30 3.577 14.943 15.778 6.848 15.731 45.038 4.429 3.162 34.947

40 3.603 14.992 15.624 6.862 15.758 44.938 4.429 3.163 34.945

Note: The decompositions are reported for one-, 10-, 20-, 30 and 40-quarter horizons. Ordering used here is RV, GGDP, INVT, UR, INF, IR, TB, and DBD. However, changing the

order did not alter the results to any substantial degree. This is because the Choleski decomposition is used in order to orthogonalize the innovations across equations.

S. Rafiq et al. / Resources Policy 34 (2009) 121–132 129

auto-regressive (VAR) framework taking the data set for all theconcerned variables spanning from 1999Q1 (just after the lastquarter of structural break, i.e. 1998Q3) to 2006Q4. The testof time-series properties and model estimation results are asfollows.

Model estimation after 1999

As budget deficit series is found to be a I (1) process for thewhole period from1993Q1 to 2006Q4, the series from 1999Q1 to2006Q4 is further investigated by both tests of unit root and testfor structural beak. The tests indicate that the budget deficit seriesas a stationary process at the level after the date of structuralbreak. Thus, a VAR for all the variables in their levels is required tosee the actual impact of realized volatility on the macroeconomicactivities of Thailand in the recent times, more precisely after the

Banking Crisis of Asia. Moreover, for the data period from 1999Q1to 2006Q4, the SIC indicates that the appropriate lag length ofVAR is 2.

The VAR Granger causality/block exogeneity Wald Test tableindicates that, in respect of all other variables, realized volatility ofoil prices can be treated as an endogenous variable in the model asis the case for the longer period (Table 6).

The test for Granger causality for all the other variables whenRV is excluded indicates that only investment is Granger causedby oil price volatility when the data set spans from 1999Q1 to2006Q4 (Table 7).

The results of the VAR (2) output for the data period1999Q1–2006Q4 is presented in Appendix Table A1, and theimpulse response functions are given in Fig. 3. The impulseresponses for the period after the banking crisis (1999Q1–2006Q4) show that, in most of the cases, realized volatility hasits impact on the shorter time horizon and the highest impactwould happen to investment, interest rate, and budget deficit.

The results of variance decomposition over a period of a 40-quarter time horizon for different variables for the period of1999Q1–2006Q4 are presented in Table 8. The results of variancedecomposition show that, oil price volatility explains a fairportion of innovations in GDP growth, investment, and budgetdeficit over a longer time period horizon.

Conclusions and policy implications

This paper empirically examines the impact of oil pricevolatility on key macroeconomic variables in Thailand byusing vector auto-regression systems. The empirical resultsfrom the Granger causality test show that there is unidirectionalcausality running from oil price volatility to investment,unemployment rate, interest rate and trade balance for thewhole data period. The impulse responses show that, inmost of the cases, realized volatility has an impact on the shortertime horizon, with the highest impact on investment andunemployment rate. Like Cunado and Gracia (2005) and Guoand Kliesen (2005), this study finds that the nature of therelationship between oil price volatility and economic indi-cators is a short-term one. Results from the impulse responsefunctions and variance decomposition confirm that a fairportion of fluctuations in unemployment rate and investment isexplained by oil price volatility. All these findings supportthe popular notion of the impact of uncertainty on delaying

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-0.01

0.00

0.01

0.02

5 10 15 20 25 30 35 40

Response of RV to RV

-3

-2

-1

0

1

2

5 10 15 20 25 30 35 40

Response of GGDP to RV

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

5 10 15 20 25 30 35 40

Response of INV to RV

-0.4

0.0

0.4

0.8

5 10 15 20 25 30 35 40

Response of UR to RV

-0.12

-0.08

-0.04

0.00

0.04

0.08

0.12

5 10 15 20 25 30 35 40

Response of INF to RV

-3

-2

-1

0

1

2

5 10 15 20 25 30 35 40

Response of IR to RV

-2

-1

0

1

2

5 10 15 20 25 30 35 40

Response of TB to RV

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

5 10 15 20 25 30 35 40

Response of BD to RV

Fig. 3. Responses from RV, GGDP, INVT, UR, INF, IR, TB and DBD to Cholesky One S.D. Innovations+S.E in RV from 1999Q1 to 2006Q4.

Table 8Bivariate variance decomposition of RV with DGGDP, INV, UR, INF, IR, DTB, and DBD

Period S.E. RV GGDP S.E. RV INV S.E. RV UR S.E. RV INF

1 1.798 3.827 96.173 0.456 14.510 84.647 0.206 0.836 65.749 0.0596 5.192 51.007

10 4.078 17.765 66.125 0.983 6.0234 21.783 1.6284 1.024 27.713 0.17089 6.412 48.013

20 5.073 16.708 67.734 1.051 6.2014 19.617 1.801 0.889 27.126 0.262 4.620 50.372

30 5.589 16.211 68.545 1.104 6.3788 18.129 1.827 0.971 27.006 0.3684 4.127 51.861

40 5.913 15.970 68.095 1.1545 6.3248 17.079 1.856 0.991 28.437 0.495 3.842 52.849

Period S.E. RV IR S.E. RV TB S.E. RV BD

1 2.772 2.831 75.221 1.746 0.000 35.649 0.409 7.444 46.594

10 4.543 9.889 33.689 3.505 5.424 22.630 0.844 17.375 14.158

20 5.207 8.459 27.819 4.315 6.427 18.211 1.048 14.177 9.371

30 5.544 8.254 25.529 5.150 6.600 18.079 1.303 10.448 6.137

40 5.819 7.958 23.801 6.049 5.954 17.872 1.624 8.287 4.027

Note: The decompositions are reported for one-, 10-, 20-, 30- and 40-quarter horizons. Ordering used here is RV, GGDP, INVT, UR, INF, IR, TB, and BD. However, changing the

order did not alter the results to any substantial degree. This is because the Choleski decomposition is used in order to orthogonalize the innovations across equations.

S. Rafiq et al. / Resources Policy 34 (2009) 121–132130

and reducing investment, which consequently transmits tounemployment through sectoral restructuring and resourcereallocation.

It can be inferred from the VAR system for the full periodthat oil price volatility has significant impact on growth, employ-

ment and investment. However, since Perron (1997) testidentifies structural breaks in all the variables around1997–1998 (during the Asian Financial Crisis), the study decom-poses the whole time period into two and employs VARanalysis for the period of 1999Q1–2006Q4. The results after the

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S. Rafiq et al. / Resources Policy 34 (2009) 121–132 131

financial crisis show that adverse effect of oil price volatilityhas been mitigated to some extent. During this period budgetdeficit has been affected most, which may be due to the changein exchange rate regime that put higher pressure on oil fund.It seems that oil subsidization plays significant role in improvingeconomic performance by lessening the adverse effect ofoil price volatility on macroeconomic indicators. The policyimplication of this result would be that the governmentshould keep pursuing its policy to stabilize domestic oil

Table A1

RV GGDP INV UR

RV(�1) 0.176607 �63.27385 1.212755 1.7482

(0.26109) (33.7418) (8.55586) (3.8608

RV(�2) �0.570845 �4.517563 �26.12685 �2.4522

(0.35744) (46.1928) (11.7130) (5.2855

GGDP(�1) �0.002041 �0.232980 �0.125123 �0.0497

(0.00177) (0.22849) (0.05794) (0.0261

GGDP(�2) �0.001874 �0.634877 0.065793 �0.0590

(0.00148) (0.19083) (0.04839) (0.0218

INV(�1) 0.004188 0.199843 0.300970 0.3575

(0.00828) (1.07019) (0.27137) (0.1224

INV(�2) �0.005463 �1.509865 �0.550350 0.1658

(0.00876) (1.13189) (0.28701) (0.1295

UR(�1) 0.012968 1.236178 �0.361000 0.7814

(0.01315) (1.69912) (0.43084) (0.1944

UR(�2) �0.013883 �1.080146 0.337705 �0.0147

(0.01146) (1.48098) (0.37553) (0.1694

INF(�1) 0.025648 �4.703893 0.076855 0.4959

(0.04418) (5.70943) (1.44773) (0.6532

INF(�2) �0.056236 1.818193 0.546661 1.3247

(0.04629) (5.98183) (1.51680) (0.6844

IR(�1) 0.000138 0.037615 �0.132176 0.0098

(0.00132) (0.17113) (0.04339) (0.0195

IR(�2) 0.000630 0.017428 0.001401 0.0154

(0.00160) (0.20660) (0.05239) (0.0236

TB(�1) �0.003541 �0.318590 �0.258664 �0.1287

(0.00304) (0.39324) (0.09971) (0.0450

TB(�2) 0.000833 0.751676 0.080683 �0.1036

(0.00285) (0.36869) (0.09349) (0.0421

BD(�1) 0.004656 0.019620 0.003840 �0.0384

(0.00931) (1.20259) (0.30494) (0.13760

BD(�2) �0.002426 �0.134277 �0.149265 �0.0657

(0.00832) (1.07473) (0.27252) (0.1229

C 0.075894 8.496136 1.324581 �1.4244

(0.06187) (7.99610) (2.02756) (0.9149

Notes: Standard errors are reported in ( ).

price through subsidization and thus help boost investment,employment, and growth.

Appendix

For VAR (1) output for Thailand for the period of1999Q1–2006Q4 see Table A1.

INF IR TB DBD

80 0.607179 �39.37850 �43.66341 14.38598

6) (1.11819) (52.0271) (32.7704) (7.69104)

31 2.025462 �59.85878 �31.12911 3.831760

4) (1.53080) (71.2254) (44.8629) (10.5291)

46 0.004445 0.113808 �0.018680 0.077189

4) (0.00757) (0.35232) (0.22191) (0.05208)

71 0.011536 �0.037956 0.514596 �0.026430

4) (0.00632) (0.29424) (0.18534) (0.04350)

81 �0.005581 0.304139 �1.575878 �0.129416

6) (0.03547) (1.65015) (1.03938) (0.24394)

82 0.014027 0.468762 �1.128605 0.062441

2) (0.03751) (1.74528) (1.09931) (0.25800)

99 �0.015431 �0.558082 0.140399 �0.081181

2) (0.05631) (2.61990) (1.65020) (0.38729)

73 0.008116 1.913536 �0.098160 0.132217

6) (0.04908) (2.28354) (1.43834) (0.33757)

49 0.280802 �11.95022 2.225445 �1.506360

9) (0.18921) (8.80346) (5.54505) (1.30139)

80 0.696311 �4.116152 2.139267 �0.128571

6) (0.19823) (9.22347) (5.80961) (1.36348)

29 �0.001258 0.044974 0.031565 �0.026240

8) (0.00567) (0.26386) (0.16620) (0.03901)

73 0.002622 �0.052030 �0.180121 �0.027026

4) (0.00685) (0.31856) (0.20065) (0.04709)

37 0.007185 0.444279 0.226425 0.019350

0) (0.01303) (0.60634) (0.38192) (0.08963)

22 0.003647 0.330218 0.023471 �0.037011

9) (0.01222) (0.56849) (0.35807) (0.08404)

53 �0.026735 0.959234 0.658712 0.481852

) (0.03985) (1.85429) (1.16797) (0.27412)

91 �0.001447 0.152756 �0.594786 �0.296999

7) (0.03562) (1.65715) (1.04379) (0.24497)

87 0.068964 16.17972 �2.563754 3.204182

4) (0.26499) (12.3293) (7.76589) (1.82261)

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References

Aıt-Sahalia, Y., Mykland, P.A., Zhang, L., 2005. How often to sample a continuous-time process in the presence of market microstructure noise. The Review ofFinancial Studies 18 (2), 351–416.

Amano, R.A., Norden, S., 1998. Exchange rates and oil prices. Review ofInternational Economics 6 (4), 683–694.

Andersen, T.G., Bollerslev, T., Diebold, F.X., Ebens, H., 2001a. The distribution ofrealized stock return volatility. Journal of Financial Economics 61 (1), 43–76.

Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2001b. The distribution ofrealized exchange rate volatility. Journal of the American Statistical Association96, 42–55.

Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2003. Modeling andforecasting realized volatility. Econometrica 71 (2), 579–625.

Andersen, T.G., Bollerslev, T., Meddahi, N., 2004. Analytical evaluation of volatilityforecasts. International Economic Review 45 (4), 1079–1110.

Barndorff-Nielsen, O.E., Shephard, N., 2001a. Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics. Journal of RoyalStatistical Society, Series B 63 (2), 167–241.

Barndorff-Nielsen, O.E., Shephard, N., 2002. Econometric analysis of realizedvolatility and its use in estimating stochastic volatility models. Journal of RoyalStatistical Society, Series B 64 (2), 253–280.

Bernanke, B.S., 1983. Irreversibility, uncertainty, and cyclical investment. QuarterlyJournal of Economics 98 (1), 85.

Birol, F., 2007. Oil Market Outlook and Policy Implications /www.worldenergyout-look.org/speeches.aspS (accessed on 3 December 2007).

Bloch, H., Dockery, A.M., Sapsford, D., 2006. Commodity prices and the dynamics ofinflation in commodity-exporting nations: evidence from Australia andCanada. Economic Record 82, 97–109.

Brooks, C., 2002. Introductory Econometrics for Finance. Cambridge UniversityPress, Cambridge.

Burbridge, J., Harrison, A., 1984. Testing for the effects of oil price rises using vectorautoregressions. International Economic Review 25 (2), 459–484.

Chaudhuri, K., Daniel, B.C., 1998. Long-run equilibrium real exchange rates and oilprices. Economics Letters 58 (2), 231–238.

Chen, S.-S., Chen, H.-C., 2007. Oil prices and real exchange rates. Energy Economics29 (3), 390–404.

Cologni, A., Manera, M., 2008. Oil prices, inflation and interest rates in a structuralcointegrated VAR model for the G-7 countries. Energy Economics 30 (3),856–888.

Cunado, J., Gracia, F.P.d., 2005. Oil prices, economic activity and inflation: evidencefor some Asian countries. Quarterly Review of Economics and Finance 45 (1),65–83.

Dohner, R.S., 1981. Energy prices, economic activity and inflation: survey of issuesand results. In: Mork, K.A. (Ed.), Energy Prices, Inflation and Economic Activity.Ballinger, Cambridge, MA.

Ferderer, J.P., 1996. Oil price volatility and the macroeconomy. Journal ofMacroeconomics 18 (1), 1–26.

Gisser, M., Goodwin, T.H., 1986. Crude oil and the macroeconomy: tests of somepopular notions. Journal of Money, Credit and Banking 18 (1), 95–103.

Granger, C.W.J., 1969. Investigating causal relations by econometric models andcross-spectral methods. Econometrica 37 (3), 424–438.

Guo, H., Kliesen, K.L., 2005. Oil price volatility and US macroeconomic activity.Review—Federal Reserve Bank of St. Louis 57 (6), 669–683.

Hacker, R.S., Hatemi-J, A., 2005. The effect of regime shifts on the longrunrelationships for Swedish money demand. Applied Economics 37, 1131–1136.

Hamilton, J.D., 1983. Oil and the macroeconomy since World War II. Journal ofPolitical Economy 91 (2), 228–248.

Hamilton, J.D., 1988. A neoclassical model of unemployment and the businesscycle. Journal of Political Economy 96 (3), 593–617.

Hamilton, J.D., 1996. This is what happened to oil price–macroeconomy relation-ship. Journal of Monetary Economics 38, 215–220.

Hamilton, J.D., 2008. Oil and the macroeconomy. In: Durlauf, S., Blume, L. (Eds.),New Palgrave Directory of Economics, second ed. Palgrave McMillan Ltd.

Jimenez-Rodriguez, R., Sanchez, M., 2005. Oil price shocks and real GDP growth:empirical evidence for some OECD countries. Applied Economics 37 (2),201–228.

Lardic, S., Mignon, V., 2006. The impact of oil prices on GDP in European countries:an empirical investigation based on asymmetric cointegration. Energy Policy34 (18), 3910–3915.

Lee, K., Ni, S., 1995. Oil shocks and the macroeconomy: the role of price variability.Energy Journal 16 (4), 39–56.

Lilien, D.M., 1982. Sectoral shifts and cyclical unemployment. Journal of PoliticalEconomy 90 (4), 777–793.

Lisman, J.H.C., Sandee, J., 1964. Derivation of quarterly figures from annual data.Applied Statistics 13 (2), 87–90.

Loungani, P., 1986. On price shocks and the dispersion hypothesis. Review ofEconomics and Statistics 68 (3), 536–539.

Lumsdaine, R.L., Papell, D.H., 1997. Multiple trend breaks and the unit-roothypothesis. The Review of Economics and Statistics 20, 411–423.

Mork, K.A., 1989. Oil and the macroeconomy when prices go up and down: anextension of Hamilton’s results. Journal of Political Economy 97 (3), 740–744.

Mork, K.A., Olsen, O., 1994. Macroeconomic responses to oil price increases anddecreases in seven OECD countries. Energy Journal 15 (4), 19–35.

Mory, J.F., 1993. Oil prices and economic activity: is the relationship symmetric?The Energy Journal 14 (4), 151–161.

Pahlavani, M., 2005. The relationship between trade and economic growth in Iran:an application of a new cointegration technique in the presence of structuralbreaks. Working Paper no. 05/28 University of Wollongong, NSW, Australia.

Perron, P., 1989. The great crash, the oil price shocks, and the unit root hypothesis.Econometrica 57 (6), 1361–1401.

Perron, P., 1997. Further evidence on breaking trend functions in macroeconomicvariables. Journal of Econometrics 80 (2), 355–385.

Salim, R.A., Bloch, H., 2007. Business expenditure on R&D and trade performancesin Australia: is there a link? Applied Economics November, 1–11.

Salman, A.K., Shukur, G., 2004. Testing for Granger causality between industrialoutput and CPI in the presence of regime shift: Swedish data. Journal ofEconomic Studies 31 (5–6), 492–499.

Sims, C.A., 1980. Macroeconomics and reality. Econometrica 48, 1–48.Uri, N.D., 1995. The impact of crude oil price volatility on agricultural employment

in the US. The Journal of Energy and Development 20 (Spring), 269–288.Zhang, L., Mykland, P.A., Aıt-Sahalia, Y., 2005. A tale of two time scales:

determining integrated volatility with noisy high-frequency data. Journal ofthe American Statistical Association 100 (472), 1394–1411.

Zhou, S., 1995. The response of real exchange rates to various economic shocks.Southern Economic Journal 61 (4), 936–954.

Zivot, E., Andrews, D.W.K., 1992. Further evidence on the great crash, the oil-priceshock, and the unit-root hypothesis. Journal of Business and EconomicStatistics 10 (3), 20.