the effect of exogenous oil supply shocks on major asean countries

A Contemporary Business Journal

ISSN: 2232-0172 Vol 4 Issue 1, February 2014

pp. 55-85

Taylor’s Business Review, Vol. 4 Issue 1, February 2014 55

The Effect of Exogenous Oil Supply Shocks on Major ASEAN Countries

Amir Ranjbar*University Malaya

Saeed Pahlevan Sharif**Taylor’s University

Abstract: How do shortfalls in crude oil production caused by wars and other exogenous political events in oil-producing countries affect oil prices, economic growth and inflation in major ASEAN countries? In this study, we examine and compare the effects of these shocks on inflation rate and real growth rate of five major ASEAN countries: Indonesia, Malaysia, Philippines, Singapore, and Thailand. In addition, the stagflationary effect of those shocks on the economies of these countries is also studied.

Key words: Oil shocks, economic fluctuations, war, economic growth, ASEANJEL classification: C53, F20

1. INTRODUCTION

Economists have long been engrossed in empirical evidence that suggests oil price shocks may be closely related to macroeconomic performance. The interest in this topic originates back to the early 1970s and its theory has been shaped by the economic experience of the 1970s and early 1980s. Moreover, 1970s was an era of increasing dependence on imported oil, unprecedented disruptions in the global oil market and poor macroeconomic performance in the United States. Thus, it was plausible to suspect a causal relationship between oil prices and U.S. macroeconomic aggregates.

In view of the above foregoing arguments however, there is no conviction that exogenous oil supply is to be blamed for the economic malaise of the 1970s. On the basis of such scenario, it can be predicted that history might repeat itself. However, while the experience of the 1970s continues to play a major role in the discussions linking oil and macroeconomy, there have been a number of new “oil price shocks” since the 1970s. For example, the 1986 collapse of oil prices, the 2000 boom, and the oil price increases following the 1990-1991 Gulf war and the 2003 Iraq war. All the above shocks were accompanied by

* Faculty of Business and Accountancy, University Malaya. Email: [email protected]** Business School, Taylor’s University. Email: [email protected]

The Effect of Exogenous Oil Supply Shocks on Major ASEAN CountriesAmir Ranjbar and Saeed Pahlevan Sharif

Taylor’s Business Review, Vol. 4 Issue 1, February 201456

periods of excessive inflation, reduced productivity and lower economic growth. Therefore, it is even more vital to comprehend the underlying complications related to previous “oil shock” episodes and its corresponding effect on macroeconomic aggregates such as inflation rate and real growth rate.

There is a lot of empirical literature in this field where some consider the price shocks as the main reason while others consider production shocks. Bruno and Sachs (1985) were one of the first to analyse in depth the effects of the 1970s oil prices on output and inflation in major industrialised countries and explored the roles of monetary policy and wage setting. Hamilton (1983, 1996) showed that most US recessions were caused by an increase in the oil price. Many other researchers such as Mork (1989), Davis & Haltiwanger (2001), Muellbauer & Nunziata (2001) and Balke, Brown, & Yucel (2002) have reported a correlation between the increase in oil prices and subsequent economic downturns. While most of the studies focused on the US economy, there were also some other studies that looked at the rest of the world. Cunado & de Gracia (2003) looked at evidence in European countries, while Bohi (1989) compared the inflation and real output experiences of Japan, Germany, the UK and the US during the oil supply shocks of the 1970s.

Some other researchers such as Kilian (2006, 2009) assessed systematically the differences as well as similarities in the responses of G7 economies to exogenous oil supply shocks. The author exploited oil supply shocks that were exogenous with respect to global macroeconomic conditions.

On the other hand, some other authors are of the view that the stagflation of the 1970s was largely due to factors such as monetary policy. Barsky & Kilian (2002) argued that they may have been partly caused by exogenous changes in the monetary policy, which coincided with the rise in oil prices. Bernanke, Gertler, & Watson (1997) discussed that the main factor for the decline in GDP and employment was due to the rise in interest rates, as a result of the Federal’s endogenous response to the higher inflation induced by the oil shocks.

In this study, we used the Kilian (2006, 2009) methodology to investigate the effect of exogenous oil supply disruptions on five major ASEAN countries comprising of Indonesia, Malaysia, Philippines, Singapore, and Thailand. We wanted to identify the common patterns in the response of macroeconomic aggregates to exogenous oil supply disruptions Which ASEAN economies were the most invulnerable to exogenous oil supply shocks and which have been affected the most? How does real GDP growth respond? How long does it take for the responses to set in? Are there any systematic differences between the five countries that produce oil and those that do not? Do exogenous oil supply shocks generate sustained inflation? Are exogenous oil production shortfalls stagflationary? To what extent can the poor macroeconomic performance of five ASEAN countries during specific historical episodes be attributed to exogenous shocks in oil production?

In a section of this paper, the two different available methods to measure the effects of oil shocks will be discussed. In the section on data, we introduce the structure of the required data for each of the variables. Then we propose two linear regression models, one as a base model and the other as an alternative model to estimate the relationship between exogenous oil supply fluctuations and real growth rate and inflation rate in each of the countries. Finally, the results and conclusion are presented.



2. IDENTIFYING THE EFFECTS OF EXOGENOUS OIL SUPPLY DISRUPTIONS

2.1 Oil Price-Based Measures

Early studies sometimes treated the price of oil (or positive changes in the price) as the measure of exogenous oil supply disruption. Rotemberg & Woodford (1996), Barsky & Kilian (2002) and Hamilton (2003) proved that at least since 1973, the price of oil has been endogenous to global macroeconomic conditions and cannot be treated as exogenous. Although three of the largest oil price increases since the early 1970s occurred near periods of large exogenous shocks to oil production, not all exogenous oil supply shocks have been associated with net oil price increases. In addition, it is commonly known that net oil price increases may arise even in the absence of exogenous political events in the Middle East as a result of strong demand for industrial commodities at large. Thus, net oil price increases are not an appropriate measure of exogenous oil supply shocks.

2.2. Oil Production-Based Measures

There are other measures of exogenous oil supply shocks that can be identified using information on observable changes in the production levels of oil-producing countries that are subject to exogenous political shocks. For example, according to Hamilton (2003), the difference in production levels over the period in question is expressed as a share of the average world oil production in the year, in which the exogenous event started. The resulting “production shortfall” is treated as a measure of the magnitude of the shock that occurred in the first quarter of the exogenous event. An alternative measure of exogenous oil supply shocks proposed by Kilian (2006) is based on crude oil production data for OPEC countries and non-OPEC countries that are available from the U.S. Department of Energy. This measure is based on the observation that any attempt to identify the timing and magnitude of exogenous production shortfalls requires explicit assumptions about the counterfactual path of crude oil production in the absence of the exogenous event. Using suitable assumptions about the counterfactual path of oil production based on the evolution of oil production in other oil-producing countries, the exogenous production shortfall is constructed as the difference between the actual path of crude oil production and the counterfactual path. The change over time in this exogenous production shortfall series (aggregated across OPEC countries and expressed as a percent share of world oil production) provides a natural measure of the exogenous oil supply shock. The analysis in this paper utilised the baseline time series of exogenous oil supply shocks developed by Kilian (2006).



3. DATA

3.1. Exogenous Oil Production Shocks

Table 1 shows a list of key dates in oil production.Table 1. Key dates in oil productionDate Political eventOctober 1973 Yom-Kippur war, Arab oil embargoOctober 1978 Iranian RevolutionSeptember 1980 Iran-Iraq WarAugust 1990 Persian Gulf WarDecember 2002 Civil unrest in VenezuelaMarch 2003 Iraq War

The measure of exogenous oil supply shocks used in this empirical analysis is based on monthly crude oil production data by country provided by the U.S. Department of Energy. The approach can be summarised as follows: Any attempt to identify the timing and magnitude of an exogenous production shortfall requires explicit assumptions about the counterfactual path of oil production in the absence of the exogenous event in question.

For a given exogenous event, say a war in some OPEC country, the first step is to identify a group of oil-producing countries that is subject to the same global macroeconomic conditions and same economic incentives as the war-stricken country but whose production is not affected by the war. The countries that belong in this benchmarked group must be decided on a case by case basis drawing on historical accounts and industry sources. The counterfactual production level for the country affected by the war is generated by extrapolating its pre-war production level based on the average growth rate of production in the benchmarked countries.

Table 2 illustrates the assumptions about the benchmarked countries used in constructing the counterfactual.

Table 2. Production benchmarks used in constructing the exogenous crude oil production shortfalls OPEC countries subject to exogenous production shocks in Jan 1971 - June 2008 Saudi Algeria Iran Iraq Kuwait Libya Qatar Arabia UAE VenezuelaJan 1971 - October 1973Nov 1973 - March 1974 A A A A A A AOct 1978 - April 1979 B B BMay 1979 - July 1990 B BAug 1990 - March 1991 C C C CApril 1991 - June 2002 C C CJuly 2002 - Nov 2002 C C C CDec 2002 - Oct 2003 D D D D DNov 2003 - Sept 2004 D D D DOct 2014 - June 2006



A: Non-OPEC oil producersB: OPEC excluding Iran, Iraq, and Saudi ArabiaC: OPEC excluding Iran, Iraq, Saudi Arabia and KuwaitD: OPEC excluding Iran, Iraq, Saudi Arabia, Kuwait and Venezuela

In the second step, the exogenous production shortfall for each OPEC country is obtained by constructing the difference between actual crude oil production in that country and the counterfactual crude oil production at each point in time, resulting in a time series for each of the nine OPEC countries which represents that country’s exogenous shortfall of crude oil production (Table 2). The aggregate measure of the exogenous crude oil production shortfall for the whole of OPEC is obtained by summing these time series. Finally, the exogenous OPEC oil supply shock is constructed by normalising that aggregate shortfall series as a percent of world crude oil production and taking the first differences. This approach amounts to treating the exogenous OPEC production shortfall series as a random walk and is consistent with the lack of serial correlation in the differences.

Figure 1 shows the exogenous OPEC oil supply shock series derived on the basis of Table 2 and suitably aggregated to a quarterly frequency. The major oil dates have been imposed in Figure 1 as vertical lines. As expected, the most important spikes in the series occur near those dates. Unlike existing quantitative dummy measures of exogenous oil supply shocks, the series in Figure 1 contains negative as well as positive shocks. This feature is essential as many historical production shortfalls triggered by exogenous events have been at least partially reversed. Thus the corresponding change in the exogenous production shortfall should be characterised by an initial negative spike followed by one or more subsequent positive spikes. Only if the exogenous OPEC production shortfall was permanent, would there be no positive realisations of the oil supply shock measure. As Figure 1 shows, virtually all exogenous shortfalls were followed by at least a partial reversal. The positive values of the oil shock measure also reflect the fact that wars in the Middle East may actually cause higher oil production over time, when the parties involved resort to oil exports to finance the war. Another key difference is that this new measure allows for repeated exogenous oil supply shocks of the same sign. A good example is the period following the Persian Gulf War.

Figure 1. Measure of exogenous oil supply shocks



3.2. Real GDP and CPI Inflation

The regression analysis is based on seasonally adjusted quarterly real GDP growth and CPI inflation rates for January 1971-June 2008. The CPI data and the GDP data for Philippines were obtained from the IFS website. The Singapore GDP data were sourced from Singapore Statistics, and Malaysia GDP data from Treasury Malaysia. For Indonesia and Thailand, we used World Bank data.

4. METHODOLOGY

4.1. Base Linear Regression Model

Given a production-based measure of the exogenous oil supply shock, there are two alternative approaches to quantifying the dynamic effects of exogenous fluctuations in oil production on macroeconomic aggregates. The first approach (Hamilton, 2003) is to use lags in the exogenous oil supply shock to identify regressions that relate the macroeconomic aggregate of interest to past oil price changes and past values of macroeconomic aggregates. Let xt denote the date t observation of the exogenous oil supply shock series, Δyt. the corresponding percent growth rate in real GDP and pt , the percent change in the consumer price index. The objects of interest are the impulse responses δΔyt +i /δxt and δpt +i /δxt, i =1, 2, 3,… For each country, the first-order effect of a given increase in xt on Δyt +i and pt +i , respectively, may be computed based on the fitted value of the linear ordinary least squares (OLS) regressions:

4 8

Δyt = ∝ +∑ βi Δyt – i +∑ γj

xt – j+ ut (1) i =1 j = 0

4 8

pt = δ +∑ λi pt – i +∑ hj

xt – j+ vt (2) i =1 j = 0

where the error terms ut and vt are serially uncorrelated, given the inclusion of four lags of the dependent variable and eight lags of the exogenous oil supply shock. Provided that the exogenous oil supply shock regressors are not correlated with any omitted exogenous variables, the implied impulse responses will measure the causal effects of the exogenous variations in oil supply.

The estimated responses provide a measure of the expected response of macroeconomic aggregates to exogenous oil production shortfalls based on historical data. They represent consistent estimates of the causal effects of a unit change in the exogenous oil supply shock measure.

4.1.1 Testing the Specification of the Baseline ModelIn models (1) and (2), we first need to prove that the oil production shock is pre-

determined. Consider the following models:



xt = θz t +∑ a1i xt – i +∑ β1j

zt – j+ e1t (3) i j

z t = gxt +∑ a2i xt – i +∑ β2j

zt – j+ e2t (4) i j

Equations (3) and (4) are representatives of a bidirectional relationship. Equation (3) shows the effect of growth rate in real GDP or Consumer Price Index on oil production shocks while equation (4) shows the reverse effect. We say that xt is pre-determined if θ = 0. If β1j=0 ∀j. Under strict exogeneity, there is no current or lagging feedback from z t to xt , and we can consistently estimate the effect of a change in xt on z t based on equation (4) alone. The equations (1) and (2) above are specific examples of equation (4).

A central proposition of this paper is that xt is strictly exogenous as described in section 2. Strict exogeneity involves two conditions: (1) Pre-determinedness of the oil supply shock series and (2) Granger non-causality from macroeconomic aggregates to the oil supply shock series. While the second condition is testable, the first one is not. Table 3 reports asymptotic p-values for the same type of Granger non-causality tests applied to xt.

Table 3. p-Values of Granger Non-Causality TestsH0: β1j=0 ∀j

Lagged Real GDP Growth Lagged CPI Inflation

Indonesia 0.528 0.911Malaysia 0.899 0.737Philippines 0.434 0.561Singapore 0.960 0.159Thailand 0.246 0.325

Table 3 shows that the null of no Granger causality cannot be rejected at the 5% level for any country. The p-values range from 24% to 96% in lagged real GDP growth and they range from 16% to 91% in lagged CPI inflation. These test results suggest that the data are consistent with the absence of feedback from inflation and real growth to the oil shock series, as required by strict exogeneity.

Equation (4) in general allows for a contemporaneous effect from xt on z t . Table 4 formally tests the null that g = 0 in equation (4). There is no evidence of a contemporaneous link from the exogenous oil supply shock to CPI inflation. No test result is significant at the 5% level. The p-values range from 5.3% to 93.4%, depending on the country. For real GDP growth, p-values generally range from 5.3% to 52%, except for Indonesia and Philippines, suggesting that the contemporaneous regressor may be safely omitted from equations (1) and (2). It seems implausible that a contemporaneous link would exist only for two countries and not for any other country. In the baseline results reported below, we therefore impose g = 0 for all regressions.



Table 4. p-values of tests for the exclusion of the contemporaneous effect of the exogenous oil supply shock

H0: g = 0 Real GDP Growth CPI Inflation

Indonesia 0.027 0.095Malaysia 0.527 0.934Philippines 0.009 0.478Singapore 0.053 0.053Thailand 0.368 0.782

4.2. Alternative Linear Regression Model

Kilian (2006) proposed an alternative regression approach that will form the basis of the analysis in this paper. This alternative approach follows the convention in the literature of treating changes to oil production induced by political events such as wars or revolutions in the Middle East as exogenous with respect to macroeconomic aggregates. Specifically, it treats the oil supply shock series as strictly exogenous in the sense that there is no feedback from current or lagging values of the dependent variable to the exogenous variable.

12

Δyt = ∝ +∑ fi xt – j + ut (5)

i =1

12

pt = δ +∑ yi xt – j + vt (6) i =1

That model shares with the baseline model the assumption that xt is predetermined. It differs from the baseline model in that it does not impose the Granger non-causality restriction. Equation (5) also relaxes the assumption that the data are well approximated by a linear VAR representation. Thus, it is of some interest to compare the response estimates from the alternative regression models (5) and (6) to the estimates from the more tightly parameterised baseline models (1) and (2). As in the baseline model, there is no instantaneous feedback from t and x to inflation and real growth.

5 RESULTS

5.1 Real Growth and Inflation Responses in the Baseline Model

Using the estimates of models (1) and (2), assessing the impact of an exogenous 1% reduction in global oil production by simulation became more straightforward. The dynamic responses to an exogenous 1% permanent reduction in crude oil production are shown in Table 5 along with the corresponding response levels of real GDP and CPI.



5.1.1 IndonesiaIn Indonesia, the exogenous oil supply shocks showed its negative effect on real GDP rate exactly one quarter after the event and no effect thereafter. This effect took place sooner in comparison with other countries. Also, the shocks had a positive effect on inflation only after one quarter.

5.1.2 MalaysiaFor Malaysia, the real GDP growth had two different reactions. On one hand, we saw a significant negative effect in the second quarter after the shocks. On the other hand, the fifth and sixth quarters showed a significant positive reaction to the shocks. Similar to Indonesia, we observed a significant increase in the inflation rate after one quarter.

5.1.3 PhilippinesIn Philippines, we only observed a significant decrease in real GDP growth after eight quarters, and surprisingly, there were significant increases in four to six quarters after the shock period. Also, in contrast to all the other countries, not only the shocks did not cause any increase in the inflation rate, but there was a decrease after one quarter. The observations revealed completely different reactions in Philippines.

5.1.4 SingaporeIn Singapore, a significant negative effect of exogenous oil shocks on real GDP growth was observed at the fourth quarter after the shock and the inflation rate had a significant increase after the sixth quarter.

5.1.5 ThailandIn Thailand, we observed two different reactions. On one hand, there were significant drops at 5% level in the second and seventh quarters after the shocks, while on the other hand, the real GDP growth had significant increases in the fourth and fifth quarters.

Table 5. Dynamic effect of a permanent 1% world oil supply disruption. OLS point estimates from baseline model

Country Significant Growth Lags Significant Inflation Lags Lag t-statistics p-value Lag t-statistics p-value

Indonesia 1 -3.229 0.0016 1 -2.199 0.0296Malaysia 2 -4.578 0.0000 2 -2.641 0.0093 5 2.170 0.0318 6 3.551 0.0005Philippines 4 2.586 0.0108 1 -2.311674 0.0224 5 2.534 0.0125 6 2.324 0.0217 8 -2.931 0.0040Singapore 1 2.729 0.0072 6 2.020 0.0455 4 -2.933 0.0040



Table 5 (con’t)Country Significant Growth Lags Significant Inflation Lags Lag t-statistics p-value Lag t-statistics p-value

Thailand 2 -3.316 0.0012 4 3.202 0.0017 - - - 5 4.971 0.0000 7 -2.706 0.0077

5.1.6 Quantifying the Stagflationary Effects in the Baseline ModelA popular notion is that exogenous oil supply shocks can be held responsible for triggering stagflation, defined as the simultaneous event of rising price levels and falling output. Figure 2 provides a formal assessment of this proposition based on the conditional co-movement measure proposed by Den Haan (2000). In Figure 2, this measure was applied to the responses of CPI inflation and real GDP growth to an exogenous oil supply disruption. Following Den Haan and Summer (2004), the plot shows conditional covariances rather than conditional correlations. This normalisation facilitates a comparison of the statistics across horizons. The conditional covariance at horizon, h, is constructed as

imp impc(h) =

Δyh ph (7) imp where z h denotes the response of variable zt at horizon h to a 1% exogenous oil supply disruption. Stagflation in the form of rising prices and falling output will cause this measure to be negative.

Figure 2. Stagflationary effects of a 1% world oil supply disruption in baseline model



5.2 Real Growth and Inflation Responses in the Alternative Model

Given estimates of models (5) and (6), it was straightforward to assess the impact of an exogenous 1% reduction in global oil production by simulation. The dynamic responses to an exogenous 1% permanent reduction in crude oil production are shown in Table 6 along with the corresponding responses of the real GDP and CPI levels.

5.2.1 IndonesiaIn the case of Indonesia, the alternative model showed the same effect as baseline model for real GDP growth. We observed a decrease in real GDP growth after one quarter. However, in contrast with the baseline model, we did not observe any significant CPI inflation in any quarter.

5.2.2 MalaysiaFor Malaysia, the alternative model showed a significant decrease in real GDP growth in the second quarter after the shocks. This finding was in accordance with the baseline model. However, unlike the baseline model in which the fifth and sixth quarters showed a significant positive reaction to the shocks, in the alternative model, we did not observe such a relationship. Similar to the baseline model, we observed a significant increase in the inflation rate after one quarter. Also, the alternative model showed a significant decrease in the inflation rate in the ninth quarter.

5.2.3 PhilippinesFor Philippines, we only observed a significant decrease in real GDP growth after eight quarters, exactly the same as the baseline model; and surprisingly, there were significant increases in four to six quarters after the shock periods. We observed a decrease in the inflation rate after one quarter, again the same as baseline model. So the results of the two models were the same for Philippines.

5.2.4 SingaporeThe results of the alternative model were completely different in the Singapore case. For example, in the baseline model, we observed a significant negative effect of exogenous oil shocks on real GDP growth in the fourth quarter whereas in the alternative model, we have it in the tenth quarter and in contrast, we observed increases in real GDP growth in one and nine quarters after the shocks. Also, the inflation rate had a significant decrease in the third and fourth quarters.

5.2.5 ThailandIn Thailand, we observed two different reactions. On one hand, there were significant drops at 5% in first, second, third and eighth quarters after shocks, while on the other hand, the real GDP growth had significant increases in the fifth and sixth quarters.

Table 6 shows the response estimates obtained from the alternative model in equations (5) and (6).



Table 6. Dynamic effect of a permanent 1% world oil supply disruption. OLS point estimates from alternative model

Country Significant Growth Lags Significant Inflation Lags Lag Coefficient t-statistics p-value Lag Coefficient t-statistics p-valueIndonesia 1 -0.563 -3.870 0.0002 - - - -Malaysia 2 -0.899 -3.219 0.0016 1 0.185 -2.768 0.0065 9 -0.135 -2.023 0.0452Philippines 4 0.388 2.450 0.0157 1 -0.444 -2.121 0.0359 6 0.383 2.422 0.0169 10 0.490 2.311 0.0225 8 -0.402 -2.517 0.0131Singapore 1 0.605 2.861 0.0049 3 -0.263 -3.189 0.0018 9 0.474 2.241 0.0268 4 -0.217 -2.659 0.0089 12 -0.472 -2.199 0.0297Thailand 1 -0.289 -2.113 0.0366 2 -0.342 -2.990 0.0034 2 -0.497 -3.628 0.0004 4 -0.244 -2.144 0.0340 3 -0.278 -2.018 0.0458 5 0.714 5.230 0.0000 6 0.352 2.595 0.0106 8 -0.328 -2.393 0.0182

5.2.6 Quantifying the Stagflationary Effects in the Alternative ModelFigure 3 shows the stagflationary effects in the alternative model. These results were also based on equation 7.

Figure 3. Stagflationary effects of a 1% world oil supply disruption in alternative model



5.3 Model Selection Results

The evidence in Table 5 and Table 6 suggest that there are some differences in the results for real GDP growth and inflation rate; therefore, the need to compare formally the fit of these two models. Given that both of our models contain exactly the same number of parameters, their ranking by the prediction mean squared error is identical to the ranking according to any information criterion.

Table 7. Comparison of the fit of the baseline and alternative modelsPrediction Mean Squared Error of Fitted Model

Real GDP Growth CPI Inflation Baseline Alternative Baseline AlternativeIndonesia 2.997 2.953 10.423 10.219Malaysia 10.835 10.869 0.918 0.622Philippines 3.512 3.538 2.686 6.126Singapore 6.872 6.250 1.664 0.931Thailand 2.632 2.610 1.965 1.351

Table 7 shows that for real GDP growth, neither model fits the data better for all countries. Since the empirical results for real GDP growth are very similar in any case, this means that for real GDP growth, we can focus on the alternative model without loss of generality.

In sharp contrast, for CPI inflation, the alternative model fits the data better than the baseline model for four countries. The improvement in fit can be substantial. Thus, based on the differences in the results for CPI inflation, the formal model selection criteria in Table 7 established conclusively that the baseline regression which imposes additional structure on the data has more statistical support. The next section will focus on the results from the baseline regression.

5.4 Comparing the Effects of Exogenous Oil Supply Shocks Across Countries and Time

Figures 4 and 5 characterise the aftermath of each of the exogenous events shown in Table 2. Each column refers to a different exogenous event, starting with the 1973/74 oil shock and ending with the oil crises of 2002/2003. The first line for each country and episode shows average real GDP growth and average CPI inflation rates. All rates have been normalised relative to their respective averages for the period January 1971-June 2008 such that a negative value corresponds to below average rates. The second line shows the average effect on real GDP growth and CPI inflation caused by exogenous oil supply shocks over the same sub-periods. All rates have been annualised. The corresponding time series plots are shown in Figures 4 and 5.



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April 1973-Feb 1975 April 1978-March 1980 April 1980-Jan 1983 March 1990-March 1993 April 2002-March 2004



6. CONCLUSION

This study estimated and compared the effects of exogenous shocks in global oil production on inflation and real output in major ASEAN countries. This comparison suggests a fair degree of similarity in the real growth responses in Indonesia, Malaysia, and Thailand which show a rapid reaction to the shocks, while this reaction is more slower in Singapore and Thailand. With regard to inflation, the reactions were also different. While Malaysia and Indonesia had quick significant increases in Consumer Price Index after one or two quarters, Singapore only experienced the same reaction after six quarters. In contrast, Thailand did not show any significant reaction to its inflation rate while surprisingly, the Consumer Price Index showed a decrease in Philippines.

References

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Hamilton, J.D. (2003). What is an oil shock? Journal of Econometrics,113, 363-398. Kilian, L. (2006). Exogenous oil supply shocks: How big are they and how much do they

matter for the U.S. economy?” The Review of Economics and Statistics, 90(2), 216-240Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks

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Muellbauer, J. & Nunziata, L. (2001). Credit, the Stock Market, and Oil. University of Oxford working paper. Oxford, UK: University of Oxford

Rotemberg, J., & Woodford, M. (1996). Imperfect competition and the effects of energy Price increases on economic activity. Journal of Money, Credit, and Banking; 28(4), 550-577



APPENDIX 1 (REGRESSION RESULTS)

A.1. Base Model

A.1.1. Indonesia Dependent Variable: GROWTH Method: Least Squares Date: 10/29/08 Time: 18:49 Sample (adjusted): 1973Q1 2008Q2 Included observations: 142 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.959611 0.258204 3.716476 0.0003 GROWTH(-1) 0.234927 0.089608 2.621705 0.0098 GROWTH(-2) -0.078749 0.091253 -0.862967 0.3898 GROWTH(-3) 0.146025 0.089155 1.637889 0.1039 GROWTH(-4) 0.033496 0.087560 0.382553 0.7027 OIL(-1) -0.460196 0.142522 -3.228955 0.0016 OIL(-2) -0.056643 0.149194 -0.379663 0.7048 OIL(-3) 0.187358 0.148455 1.262057 0.2092 OIL(-4) 0.071355 0.148336 0.481040 0.6313 OIL(-5) 0.167523 0.145362 1.152456 0.2513 OIL(-6) 0.079605 0.138518 0.574695 0.5665 OIL(-7) -0.101237 0.139590 -0.725246 0.4696 OIL(-8) 0.072022 0.140333 0.513223 0.6087

R-squared 0.183080 Mean dependent var 1.447638 Adjusted R-squared 0.107088 S.D. dependent var 1.850113 S.E. of regression 1.748246 Akaike info criterion 4.042187 Sum squared resid 394.2709 Schwarz criterion 4.312791 Log likelihood -273.9953 F-statistic 2.409191 Durbin-Watson stat 1.996212 Prob(F-statistic) 0.007548

Dependent Variable: INFLATION Method: Least Squares Date: 10/29/08 Time: 18:52 Sample (adjusted): 1973Q1 2008Q2 Included observations: 142 after adjustments


C 1.319873 0.368662 3.580172 0.0005 INFLATION(-1) 0.561703 0.080002 7.021130 0.0000 INFLATION(-2) 0.082784 0.090253 0.917245 0.3607 INFLATION(-3) -0.053851 0.089822 -0.599530 0.5499 INFLATION(-4) -0.047560 0.079231 -0.600265 0.5494



OIL(-1) 0.479220 0.217846 -2.199814 0.0296 OIL(-2) 0.022164 0.219109 0.101153 0.9196 OIL(-3) 0.079964 0.215289 0.371425 0.7109 OIL(-4) 0.086813 0.215346 0.403131 0.6875 OIL(-5) -0.183029 0.210723 -0.868577 0.3867 OIL(-6) 0.213099 0.211261 1.008698 0.3150 OIL(-7) -0.092700 0.217064 -0.427063 0.6700 OIL(-8) 0.081472 0.219248 0.371599 0.7108


A.1.2. MalaysiaDependent Variable: GROWTH Method: Least Squares Date: 10/30/08 Time: 21:27 Sample (adjusted): 1973Q1 2008Q2 Included observations: 142 after adjustments


C 1.690875 0.411360 4.110448 0.0001 GROWTH(-1) -0.048128 0.081899 -0.587654 0.5578 GROWTH(-2) -0.312491 0.081690 -3.825347 0.0002 GROWTH(-3) -0.034519 0.080789 -0.427277 0.6699 GROWTH(-4) 0.367713 0.080548 4.565159 0.0000 OIL(-1) -0.383877 0.222241 -1.727301 0.0865 OIL(-2) -1.015977 0.221903 -4.578475 0.0000 OIL(-3) -0.052614 0.235485 -0.223430 0.8236 OIL(-4) -0.270484 0.235053 -1.150738 0.2520 OIL(-5) 0.500242 0.230505 2.170205 0.0318 OIL(-6) 0.820627 0.231067 3.551475 0.0005 OIL(-7) 0.065138 0.228398 0.285194 0.7760 OIL(-8) -0.058935 0.228062 -0.258416 0.7965






C 0.266601 0.105670 2.522951 0.0129 INFLATION(-1) 0.461367 0.087106 5.296640 0.0000 INFLATION(-2) 0.258073 0.095342 2.706823 0.0077 INFLATION(-3) -0.177037 0.095301 -1.857668 0.0655 INFLATION(-4) 0.171829 0.086857 1.978284 0.0500 OIL(-1) 0.167792 0.063535 -2.640956 0.0093 OIL(-2) -0.008615 0.064970 -0.132602 0.8947 OIL(-3) -0.008470 0.064016 -0.132307 0.8949 OIL(-4) -0.068771 0.063838 -1.077285 0.2834 OIL(-5) -0.029995 0.063195 -0.474636 0.6358 OIL(-6) 0.019011 0.062483 0.304252 0.7614 OIL(-7) 0.011566 0.063733 0.181479 0.8563 OIL(-8) -0.008606 0.064233 -0.133984 0.8936


A.1.3. PhilippinesDependent Variable: GROWTH Method: Least Squares Date: 10/30/08 Time: 21:34 Sample (adjusted): 1973Q1 2008Q2 Included observations: 142 after adjustments

Variable Coefficient Std. Error t-Statistic Prob

C 0.741472 0.230835 3.212134 0.0017 GROWTH(-1) 0.014650 0.087660 0.167123 0.8675 GROWTH(-2) 0.001339 0.086701 0.015448 0.9877 GROWTH(-3) 0.242355 0.085963 2.819291 0.0056 GROWTH(-4) 0.018078 0.087052 0.207671 0.8358 OIL(-1) 0.023396 0.157491 0.148554 0.8821 OIL(-2) -0.138887 0.157214 -0.883428 0.3786



OIL(-3) 0.139746 0.155900 0.896380 0.3717 OIL(-4) 0.403452 0.156012 2.586033 0.0108 OIL(-5) 0.388742 0.153421 2.533823 0.0125 OIL(-6) 0.364999 0.157069 2.323805 0.0217 OIL(-7) -0.197487 0.162484 -1.215425 0.2264 OIL(-8) -0.474024 0.161742 -2.930739 0.0040




C 1.085431 0.257026 4.223041 0.0000 INFLATION(-1) 0.484774 0.085376 5.678073 0.0000 INFLATION(-2) 0.499503 0.090165 5.539877 0.0000 INFLATION(-3) -0.311675 0.087812 -3.549325 0.0005 INFLATION(-4) -0.079456 0.084568 -0.939552 0.3492 OIL(-1) -0.364489 0.157673 -2.311674 0.0224 OIL(-2) 0.023233 0.159142 0.145987 0.8842 OIL(-3) 0.029306 0.156947 0.186726 0.8522 OIL(-4) 0.007710 0.156870 0.049147 0.9609 OIL(-5) -0.030048 0.156022 -0.192587 0.8476 OIL(-6) 0.098086 0.154690 0.634079 0.5272 OIL(-7) -0.049932 0.157823 -0.316380 0.7522 OIL(-8) 0.192437 0.158981 1.210439 0.2283




A.1.4. SingaporeDependent Variable: GROWTH Method: Least Squares Date: 10/30/08 Time: 21:44 Sample (adjusted): 1973Q1 2008Q2 Included observations: 142 after adjustments


C 1.978261 0.359249 5.506664 0.0000 GROWTH(-1) 0.061164 0.084841 0.720930 0.4723 GROWTH(-2) 0.018981 0.083295 0.227881 0.8201 GROWTH(-3) 0.058153 0.083124 0.699588 0.4854 GROWTH(-4) -0.249821 0.080438 -3.105761 0.0023 OIL(-1) 0.592275 0.217061 2.728608 0.0072 OIL(-2) -0.026448 0.222100 -0.119082 0.9054 OIL(-3) -0.096758 0.217484 -0.444899 0.6571 OIL(-4) -0.636797 0.217146 -2.932571 0.0040 OIL(-5) -0.132003 0.217630 -0.606549 0.5452 OIL(-6) 0.214328 0.215339 0.995305 0.3215 OIL(-7) 0.380468 0.220290 1.727124 0.0865 OIL(-8) 0.054332 0.223758 0.242815 0.8085




C 0.193451 0.099737 1.939602 0.0546 INFLATION(-1) 0.610908 0.088285 6.919693 0.0000 INFLATION(-2) 0.343707 0.101692 3.379882 0.0010 INFLATION(-3) -0.288353 0.096437 -2.990064 0.0033 INFLATION(-4) 0.055735 0.084969 0.655949 0.5130



OIL(-1) -0.056901 0.081637 -0.697004 0.4871 OIL(-2) -0.133264 0.080333 -1.658905 0.0996 OIL(-3) -0.118242 0.079232 -1.492357 0.1380 OIL(-4) -0.023518 0.079303 -0.296564 0.7673 OIL(-5) 0.001065 0.079166 0.013459 0.9893 OIL(-6) 0.160208 0.079313 2.019934 0.0455 OIL(-7) -0.042423 0.082712 -0.512900 0.6089 OIL(-8) -0.033704 0.083773 -0.402321 0.6881


A.1.5. Thailand Dependent Variable: GROWTH Method: Least Squares Date: 10/30/08 Time: 21:48 Sample (adjusted): 1973Q1 2008Q2 Included observations: 142 after adjustments


C 0.683445 0.220429 3.100524 0.0024 GROWTH(-1) 0.379611 0.087919 4.317758 0.0000 GROWTH(-2) 0.102755 0.093116 1.103522 0.2719 GROWTH(-3) -0.071109 0.093911 -0.757189 0.4503 GROWTH(-4) 0.115373 0.082391 1.400320 0.1638 OIL(-1) -0.164035 0.125229 -1.309883 0.1926 OIL(-2) -0.414984 0.125165 -3.315503 0.0012 OIL(-3) -0.070975 0.124839 -0.568531 0.5707 OIL(-4) 0.399061 0.124635 3.201832 0.0017 OIL(-5) 0.631808 0.127104 4.970803 0.0000 OIL(-6) 0.111742 0.135344 0.825610 0.4105 OIL(-7) -0.368496 0.136191 -2.705730 0.0077 OIL(-8) -0.281724 0.135086 -2.085510 0.0390






C 0.391865 0.151050 2.594280 0.0106 INFLATION(-1) 0.570007 0.089067 6.399747 0.0000 INFLATION(-2) 0.113258 0.104234 1.086573 0.2793 INFLATION(-3) -0.083851 0.107224 -0.782021 0.4356 INFLATION(-4) 0.112710 0.092780 1.214814 0.2267 OIL(-1) -0.161454 0.096233 -1.677744 0.0958 OIL(-2) -0.168380 0.097510 -1.726794 0.0866 OIL(-3) 0.052520 0.094302 0.556933 0.5785 OIL(-4) -0.128496 0.094127 -1.365133 0.1746 OIL(-5) -0.016191 0.093739 -0.172723 0.8631 OIL(-6) 0.110152 0.094016 1.171626 0.2435 OIL(-7) 0.011933 0.095249 0.125277 0.9005 OIL(-8) -0.075684 0.095244 -0.794631 0.4283


A.2. Alternative Model

A.2.1.IndonesiaDependent Variable: GROWTH Method: Least Squares Date: 10/30/08 Time: 23:37 Sample (adjusted): 1974Q1 2008Q2 Included observations: 138 after adjustments


C 1.396335 0.156791 8.905699 0.0000 OIL(-1) -0.562756 0.145418 -3.869926 0.0002 OIL(-2) -0.183675 0.145593 -1.261563 0.2095 OIL(-3) 0.128582 0.146600 0.877092 0.3821



OIL(-4) -0.027558 0.145036 -0.190012 0.8496 OIL(-5) 0.116759 0.145299 0.803574 0.4232 OIL(-6) 0.134218 0.144368 0.929692 0.3543 OIL(-7) -0.099060 0.144317 -0.686404 0.4937 OIL(-8) 0.049266 0.145750 0.338014 0.7359 OIL(-9) -0.083654 0.145406 -0.575314 0.5661 OIL(-10) -0.171651 0.147136 -1.166615 0.2456 OIL(-11) 0.049101 0.147048 0.333914 0.7390 OIL(-12) 0.048631 0.147432 0.329852 0.7421




C 2.858346 0.291667 9.800023 0.0000 OIL(-1) -0.239037 0.270510 -0.883651 0.3786 OIL(-2) -0.059185 0.270836 -0.218527 0.8274 OIL(-3) -0.020487 0.272709 -0.075124 0.9402 OIL(-4) 0.052624 0.269799 0.195049 0.8457 OIL(-5) -0.166718 0.270290 -0.616812 0.5385 OIL(-6) 0.143377 0.268557 0.533877 0.5944 OIL(-7) 0.027146 0.268463 0.101116 0.9196 OIL(-8) 0.061964 0.271129 0.228539 0.8196 OIL(-9) -0.153866 0.270488 -0.568847 0.5705 OIL(-10) -0.127777 0.273707 -0.466839 0.6414 OIL(-11) -0.138114 0.273543 -0.504907 0.6145 OIL(-12) -0.041693 0.274258 -0.152020 0.8794

R-squared 0.019125 Mean dependent var 2.889794 Adjusted R-squared -0.075039 S.D. dependent var 3.239464 S.E. of regression 3.358808 Akaike info criterion 5.350515 Sum squared resid 1410.199 Schwarz criterion 5.626271 Log likelihood -356.1856 F-statistic 0.203107 Durbin-Watson stat 0.779321 Prob(F-statistic) 0.998128



A.2.2. Malaysia Dependent Variable: GROWTH Method: Least Squares Date: 10/31/08 Time: 00:00 Sample (adjusted): 1974Q1 2008Q2 Included observations: 138 after adjustments


C 1.579495 0.300798 5.251015 0.0000 OIL(-1) -0.272760 0.278979 -0.977711 0.3301 OIL(-2) -0.899204 0.279315 -3.219319 0.0016 OIL(-3) -0.037095 0.281247 -0.131895 0.8953 OIL(-4) -0.048943 0.278245 -0.175899 0.8607 OIL(-5) 0.443284 0.278752 1.590246 0.1143 OIL(-6) 0.453563 0.276965 1.637619 0.1040 OIL(-7) -0.039222 0.276867 -0.141663 0.8876 OIL(-8) -0.319192 0.279617 -1.141534 0.2558 OIL(-9) -0.327576 0.278956 -1.174293 0.2425 OIL(-10) 0.231961 0.282275 0.821754 0.4128 OIL(-11) 0.006059 0.282106 0.021479 0.9829 OIL(-12) -0.242896 0.282843 -0.858763 0.3921


Dependent Variable: INFLATIONMethod: Least SquaresDate: 10/31/08 Time: 00:02

Sample (adjusted): 1974Q1 2008Q2 Included observations: 138 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 0.823382 0.071973 11.44015 0.0000 OIL(-1) 0.184796 0.066752 -2.768389 0.0065 OIL(-2) -0.070189 0.066833 -1.050217 0.2956 OIL(-3) -0.115122 0.067295 -1.710717 0.0896 OIL(-4) -0.109092 0.066577 -1.638593 0.1038 OIL(-5) -0.123818 0.066698 -1.856397 0.0658 OIL(-6) -0.056932 0.066270 -0.859082 0.3919 OIL(-7) -0.046997 0.066247 -0.709430 0.4794 OIL(-8) -0.049269 0.066905 -0.736405 0.4629



OIL(-9) -0.135022 0.066747 -2.022902 0.0452 OIL(-10) -0.069226 0.067541 -1.024945 0.3074 OIL(-11) -0.058482 0.067500 -0.866397 0.3879 OIL(-12) -0.007236 0.067677 -0.106919 0.9150


A.2.3. Philippines

Dependent Variable: GROWTH Method: Least Squares Date: 10/31/08 Time: 00:06 Sample (adjusted): 1974Q1 2008Q2 Included observations: 138 after adjustments


C 0.960338 0.171617 5.595807 0.0000 OIL(-1) 0.058801 0.159168 0.369424 0.7124 OIL(-2) -0.210418 0.159360 -1.320390 0.1891 OIL(-3) 0.045677 0.160462 0.284656 0.7764 OIL(-4) 0.388883 0.158750 2.449655 0.0157 OIL(-5) 0.313789 0.159039 1.973034 0.0507 OIL(-6) 0.382712 0.158020 2.421925 0.0169 OIL(-7) -0.076636 0.157964 -0.485149 0.6284 OIL(-8) -0.401606 0.159533 -2.517391 0.0131 OIL(-9) 0.096967 0.159155 0.609257 0.5435 OIL(-10) -0.087097 0.161049 -0.540808 0.5896 OIL(-11) -0.197881 0.160953 -1.229434 0.2212 OIL(-12) 0.006789 0.161373 0.042071 0.9665






C 2.492169 0.225835 11.03537 0.0000 OIL(-1) -0.444282 0.209453 -2.121155 0.0359 OIL(-2) -0.160866 0.209706 -0.767104 0.4445 OIL(-3) -0.335478 0.211156 -1.588769 0.1146 OIL(-4) -0.060848 0.208903 -0.291276 0.7713 OIL(-5) -0.085445 0.209283 -0.408275 0.6838 OIL(-6) 0.105831 0.207941 0.508949 0.6117 OIL(-7) -0.021746 0.207868 -0.104615 0.9168 OIL(-8) 0.309926 0.209932 1.476314 0.1424 OIL(-9) 0.214293 0.209436 1.023193 0.3082 OIL(-10) 0.489717 0.211928 2.310771 0.0225 OIL(-11) 0.263430 0.211801 1.243759 0.2159 OIL(-12) 0.102970 0.212355 0.484897 0.6286


A.2.4. Singapore



C 1.753955 0.228094 7.689602 0.0000 OIL(-1) 0.605336 0.211549 2.861451 0.0049 OIL(-2) -0.079214 0.211804 -0.373999 0.7090 OIL(-3) -0.142479 0.213269 -0.668075 0.5053 OIL(-4) -0.413864 0.210993 -1.961508 0.0520 OIL(-5) -0.296461 0.211377 -1.402525 0.1632



OIL(-6) 0.054256 0.210022 0.258336 0.7966 OIL(-7) 0.389345 0.209947 1.854490 0.0660 OIL(-8) 0.254948 0.212033 1.202401 0.2315 OIL(-9) 0.473945 0.211531 2.240542 0.0268 OIL(-10) 0.308891 0.214049 1.443088 0.1515 OIL(-11) -0.295563 0.213920 -1.381651 0.1695 OIL(-12) -0.471581 0.214479 -2.198724 0.0297




C 0.546137 0.088049 6.202633 0.0000 OIL(-1) -0.089035 0.081662 -1.090289 0.2777 OIL(-2) -0.133607 0.081761 -1.634117 0.1048 OIL(-3) -0.262528 0.082326 -3.188883 0.0018 OIL(-4) -0.216552 0.081448 -2.658784 0.0089 OIL(-5) -0.158686 0.081596 -1.944777 0.0540 OIL(-6) 0.042266 0.081073 0.521328 0.6031 OIL(-7) -0.035595 0.081044 -0.439202 0.6613 OIL(-8) 0.005039 0.081849 0.061561 0.9510 OIL(-9) -0.091680 0.081656 -1.122765 0.2637 OIL(-10) -0.044558 0.082627 -0.539268 0.5907 OIL(-11) 0.003677 0.082578 0.044527 0.9646 OIL(-12) -0.043155 0.082794 -0.521239 0.6031




A.2.5. Thailand



C 1.431817 0.147399 9.713889 0.0000 OIL(-1) -0.288904 0.136707 -2.113314 0.0366 OIL(-2) -0.496614 0.136872 -3.628318 0.0004 OIL(-3) -0.278087 0.137818 -2.017780 0.0458 OIL(-4) 0.216776 0.136348 1.589878 0.1144 OIL(-5) 0.714420 0.136596 5.230189 0.0000 OIL(-6) 0.352172 0.135720 2.594844 0.0106 OIL(-7) -0.240370 0.135672 -1.771702 0.0789 OIL(-8) -0.327934 0.137019 -2.393339 0.0182 OIL(-9) -0.115870 0.136696 -0.847653 0.3983 OIL(-10) -0.128798 0.138322 -0.931143 0.3536 OIL(-11) 0.020084 0.138239 0.145285 0.8847 OIL(-12) -0.078581 0.138601 -0.566960 0.5718




C 1.234733 0.123244 10.01863 0.0000 OIL(-1) -0.183759 0.114304 -1.607634 0.1104 OIL(-2) -0.342239 0.114442 -2.990508 0.0034 OIL(-3) -0.140006 0.115233 -1.214978 0.2267 OIL(-4) -0.244426 0.114003 -2.144019 0.0340 OIL(-5) -0.190385 0.114211 -1.666962 0.0980 OIL(-6) -0.033777 0.113479 -0.297648 0.7665



OIL(-7) -0.009406 0.113439 -0.082913 0.9341 OIL(-8) -0.110764 0.114565 -0.966819 0.3355 OIL(-9) 0.002088 0.114294 0.018268 0.9855 OIL(-10) -0.126727 0.115655 -1.095739 0.2753 OIL(-11) -0.123428 0.115585 -1.067853 0.2876 OIL(-12) -0.035112 0.115887 -0.302987 0.7624


the effect of exogenous oil supply shocks on major asean countries

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