The Changing Effects of Oil Price Changes on Inflation

The Changing Effects of Oil Price Changes on Inflation Adviser: Professor Bwo-Nung Huang Professor Chin-Wei Yang Advisee: Bi-Juan Lee Abstract This paper segments monthly data into three periods based on historical oil events. The central purpose is to examine the relationship between real oil price changes and the inflation rates in the framework of Mork s (1989) asymmetrical model. We find: (1) a majority of countries support long-run asymmetric responses of inflation rates to real oil price increases and decreases, but they are nearly in linear relation in the three shorter periods; (2) the immediate responses of inflation to real oil price changes are mainly larger than that of lagged periods, and the cumulative impact of real oil price increase is in general larger than the cumulative impact of real oil price decrease; (3) the direction of causality from real oil price changes to the inflation is nearly unmistakable in both asymmetrical and linear cases, and it is particularly significant in a cointegration relationship; and (4) the responses of inflation rates to oil price changes are generally higher in Period I (pre-1986:12) than in Period II (1987:01-1998:12) or in Period III (post-1999:01), which is in accord with actual observation. 1

1. Introduction What is the relationship between oil prices and inflation? The price of oil and inflation are often seen as being connected within a cause and effect framework. As oil prices move up or down, inflation follows in the same direction. The reason why this happens may be that oil is a major input in the economy - it is used in critical activities such as fueling transportation or goods made with petroleum products - and if the costs of intermediate input rise, so should the cost of end output. For instance, if the price of oil rises, then it will cost more to make plastic, and a plastics company will then pass on some or all of this cost to the consumer, which raises prices and thus inflation. With respect to the role of oil price changes in the economy, more and more studies show that there is a nonlinear relationship between oil prices and economic variables. Nearly all of the empirical analyses after Mork s (1989) study have found asymmetric economic responses to oil price changes. The asymmetry question has influenced much of the research such as Mork et al. (1994), Hamilton (1996), Cuñado and Pérez de Gracia (2005), and so on. They find that no significant relationship exists between oil-economy by using only oil price change as variable. Thus, all studies after 1990 began to include a separate negative and positive oil price changes variables as an alternative specification. To this end, the technique of entering both negative and positive real oil price changes is used to perform the asymmetrical transmission in this paper. To provide a further insight, according to the historical statistics, the direct relationship between oil price and inflation was evident in the 1970s, when the cost of oil rose from a nominal price of $3 before the 1973 oil crisis to close $40 during the 1979 oil crisis. This helped cause the consumer price index (CPI), a key measure of inflation, to more than double from 41.10 1 in January 1972 to 86.30 by the end of 1980. However, this relationship between oil and inflation started to deteriorate after the 1980s. During the 1990's Gulf War (oil crisis), crude prices doubled in six months from around $20 to around $40, but CPI remained relatively stable, growing from 134.6 in January 1991 to 137.9 in December 1991. In this relationship, it is even more noticeable 1 The Consumer Price Index (CPI) is compiled by the Bureau of Labor Statistics. Consumer Price Index is based upon 1982 (base of 100). For example, the CPI of 158 indicates 58% inflation since 1982. The commonly quoted inflation rate of say 3% is actually the change in the Consumer Price Index from a year earlier. 2

during the oil price hike from 1999 to 2008, in which the monthly average nominal price of oil started rising from the recent low point ($11.32) in January 1999 to $109.05 in April 2008. During this same period, the CPI rose from 164.30 in January 1999 to 214.82 in April 2008. Judging by the data, obviously, it seems that the strong correlation between oil prices and inflation contains some degree of nonlinearity, which is consistent with prior research mentioned above. As a matter of fact, the effects of oil price changes on inflation rates may be comparatively tiny in the long run, but they could be significant over relatively short periods, i.e., Huntington (2005). Most importantly, the effects of a given change in the price of oil may vary over time. In order to capture the more accurate transmission of oil price changes to CPI inflation, we partition our whole sample into three independent periods according to the important historical events. The first period spans before December 1986, a period characterized by oil supply shocks; the second period spans from January 1987 to December 1998, a period of relative stability for oil prices; and the third spans from January 1999 to the present, a period characterized by frequent run-up oil prices. Moreover, the advantage of splitting our sample is that it allows the elasticity of inflation with respect to oil price change to vary at different periods and would give a more precise assessment of the oil pass-through parameter. The pattern of oil prices is shown in Figure 1. Real oil price plotted by the solid line is adjusted for inflation, and the dotted line is nominal oil price measured by current U.S. dollars. Note that the oil prices displayed substantial changes over the period, with two major price increases occurring in the late 1973 and 1979, a major fall in 1986, a major price spike around the Gulf War in 1990, and a considerable rise from 1999 to 2008. The purpose of this paper is to investigate the oil price-cpi inflation relationship by means of applying Mork s (1989) asymmetrical model using monthly data. To the best of our knowledge, the analysis in this paper departs from the existing literature in several respects. First, we divide our whole data period into three subsamples by essential events in the world oil market; this allows the oil price transmission to be different over time. Moreover, it helps us to have an insight into the influence of oil price shock on inflation across different eras. Second, we employ an advanced 3

methodology in examining the effect of inflation with respect to oil price shocks, which are further separated into oil price increase and oil price decrease. Much more reasonable than the previous papers, we do not enter positive and negative oil price change as separate variables into estimated equation arbitrarily. Rather, the separate value is derived from the parameter stability test results. Specifically, we can inspect the response of inflation using positive and negative oil price change as threshold or just take a simply oil price change in different individual country at different sub-period. Finally, a majority of the earlier empirical studies have focused on the effects of oil shocks on real output, however, in this paper we put the emphasis on the responses of inflation to oil price change. This is also an important issue for economists and policy makers because oil prices and inflation are very closely correlated with the growing global economy at the present time. By using time-series data for oil prices, consumer price indexes, exchange rates, and interest rates, this paper analyzes the influence of oil price change on inflation. We formally examine three related questions. First, do oil price changes generate inflation? If so, how does inflation respond? And what is the magnitude of the response? Second, does the asymmetric response behavior exist in the sample period? If so, does the impact of oil price increase on the level of inflation rate differs from that of oil price decrease? Third, are responses of inflation to oil price shocks similar among countries across three periods? We answer these questions by exploring the shock of oil prices on inflation in 29 countries, including Austria, Belgium, Canada, Chile, Colombia, Côte d Ivoire, Denmark, Finland, France, Germany, India, Italy, Japan, Korea, Luxembourg, Malaysia, Mexico, Netherlands, Nigeria, Norway, Portugal, South Africa, Spain, Sweden, Trinidad and Tobago, the United Kingdom, the United States, Venezuela, and Taiwan. This paper is organized as follows. Next section, Section 2 provides a review of the related literature. Section 3 introduces the empirical models. Section 4 describes the data and some preliminary tests: unit root, cointegration, and the parameter instability tests before an adequate model is used to estimate the relationship between real oil prices and inflation for each country in each period. The final section, Section 5, provides concluding remarks and some policy implications derived from this research. 4

2. Literature Review A large body of empirical research has confirmed that oil price increases have strong and negative influences for the real economy (e.g., Hamilton, 1983; Burbidge and Harrison, 1984; Gisser and Goodwin, 1986; and Cuñado and Pérez de Gracia, 2003). Since the rapid fall of oil price in 1986, the established model has been challenged. There was little evidence to suggest that oil price decreases improve economic activity, in the same way that oil price increases suppress economic activity. Several authors therefore reexamined the oil price-macroeconomic relationship, using instead asymmetric or nonlinear methods (i.e., Mork, 1989; Mork et al., 1994; Lee et al., 1995; Hamilton, 1996; Hamilton, 2003; and Cuñado and Pérez de Gracia, 2005). They found that the negative linkage between oil price increases and economic activity still held. Consequently, it may be reasonable to partition oil price changes into oil price increases and decreases for the analysis of the related issue. Although a considerable amount of research has found that oil price shocks have affected the real output, only a few emphasize the effects of inflation. Quite recently, Blanchard and Galí (2007) examined the effects of the recent oil shock on output and inflation and attempted to answer why the current shocks (as in the 2000s) have had smaller effects on output and inflation than that in the 1970s. De Gregorio et al. (2007) provided a variety of estimates of the degree of transmission from oil prices to inflation over time for a large set of countries. Moreover, using a structural cointegrated VAR model for G-7 countries, Cologini and Manera (2008) found that for all countries except Japan and U.K., changes in oil prices did influence the inflation rates. In addition, some researchers suggested that oil price shocks on real GDP growth or CPI were comparatively small on average, but that they did matter in particular time period. For example, Bernanke et al. (1997) estimated their model over the whole sample and over each of the three decades (1966-75, 1976-85, and 1986-1995); Kilian (2008) focused on five specific oil shock episodes: 1973/74, 1978, 1980, 1990/91, and 2002/03, respectively. However, some problems may arise from these two studies. In the paper by Bernanke et al., the division of ten years as a subsample is arbitrary. In the Kilian paper, on the other hand, the estimates may be sensitive to the 5

choice of sample and as such may lead to potential bias due to inadequate observations. To circumvent these problems, we split our sample into three sub-periods according to the well-known historical events. This way, the ensuing results may better capture the mechanism of oil price transmission and evaluate it more accurately. 3. Methodology Following Hamilton (1996) and Hooker (1999), we use the world price of crude oil in real terms deflated by consumer price indexes as a proxy for real oil prices. Moreover, world inflation and oil prices were highly correlated during the last four decade as was indicated in Krichene (2006). Thus we use the log difference of consumer price index as a key measure of inflation rates. If only two variables are included to analyze the impact of oil prices on consumer price indexes, the estimated results might be biased due to the possibility that an oil price change can affect other economic variables such as interest rates and exchange rates as well. Since the monetary policy is the primary tool to prevent inflation. Central banks may fine-tune inflation to a significant extent through targeting interest rates. On the other hand, previous papers have found that interest rate was an important factor to be included in the discussion of the oil price-gdp relationship such as Huang et al. (2005) and Huang (2008). For this reason, we choose the interest rates as one of our control variables in the model. Moreover, exchange rate has been largely omitted from the related literature, thus the inclusion of this variable seems appropriate because it surely play a major role in monetary policy in the international economy as pointed out by Krugman (1983) and Rogoff (1991). Accordingly, it is also necessary to take exchange rate into account. As such, the study on the response of inflation from oil price changes should include oil price change ( lroilpt ), inflation rate ( lcpit ), interest rate change ( rt ), and exchange rate change ( ert ). Due to the possibility of asymmetric response of inflation from oil price change, it is necessary to take into account the asymmetry to improve the model. The asymmetrical relationship between oil price shocks and an economy is investigated in many papers such as Mork (1989), 6

Hamilton (1996), Hooker (1999), Cuñado and Pérez de Gracia (2005), and so forth. As discussed in these studies, the oil price-gdp relationship is sensitive to model specification and empirical data period (i.e. including oil price volatility of 1980s, 1990s, and later). No significant relationship exists between oil price-gdp by using only oil price change variable. As a result, all researches after the 1990s start to include separate positive and negative oil price change variables. However, issues can be raised on the prior papers that used an arbitrary asymmetrical model to allow for a separate positive and negative oil price changes without first assessing parameter stability tests of the involved variable. Hence, we utilize Hansen s (1992) parameter stability tests to perform the asymmetry test. In the case of unstable oil price change parameters, we enter real oil price increases and decreases as separate variables in the equation. Conversely, a simple oil price change variable will be taken in estimated equation when the result of stable oil price change parameters is ascertained. Given the background, the model can be specified as follows. The dependent variable of the model is the log change in CPI, and all the explanatory variables are lagged. Apart from the log change of CPI and oil price variables, there are interest rate change and exchange rate change. In absent of long-run equilibrium among the variables and with the presence of asymmetrical transmission from oil prices to inflation rates, we present the following model lcpi t p i0 lroilp i ti p i0 lroilp i t i p r p i ti i1 i1 er i ti p i1 lcpi i ti t (1) where lroilp and t lroilp are respectively real oil price increases and real oil price decreases, t lcpi, lroilp, r,and er represent respectively the difference of consumer price index and real oil price (after logarithm) as well as the difference of interest rates and exchange rates. The residual,, is assumed to be independently and identically distributed with N(0,1). The optimal lag t length in the model is chosen by minimizing the Akaike information criterion (AIC). Note that we include the current variable of oil price changes in the right hand side because it would help us to capture its current impact on inflation rates. For countries exist neither cointegration relationship among the variables nor asymmetrical 7

responses of inflation from oil price changes, equation (1) can be reduced to a linear regression model as described in equation (2): lcpi t p i0 lroilp i ti p r p i ti i1 i1 er i ti p i1 lcpi i ti t (2) If there is a cointegration relationship among the variables in conjunction with the presence of asymmetrical relation between oil price changes and inflation rates, our four-variable estimation equation can be specified as in equation (3): lcpi t p p p p p i lroilpt i i lroilpt i irt i iert i ilcpit i t1 i0 i0 i1 i1 i1 where the lcpi lroilp r er is the error correction term in t -1. For countries t1 t1 t1 t1 t1 without asymmetrical responses of inflation rates from oil price changes, equation (3) becomes a long-run linear model as expressed in equation (4): lcpi t p p p p i lroilpt i irt i iert i ilcpit i t 1 i0 i1 i1 i1 t t (3), (4) Next, for the countries with asymmetrical relations, we can carry out conventional tests of the following hypotheses H : i i, i 0,1,2,, 0 null hypothesis for all lag lengths, i.e. p i0 p. Following Frey and Manera (2007), testing the p i i0, is equivalent to testing the two hypotheses i i i and jointly. It is worth noting that we put the emphasis on both the 0 0 contemporaneous and cumulative asymmetric effect on inflation rates from oil price increase and decrease. By doing so, this analysis can shed a light on how deep and fast the oil price passes through inflation. For example, if the null hypothesis H 0 : 0 0, which implies the immediate price symmetry, is rejected, the current oil price changes have asymmetric effect on inflation rates. In the same vein, if the null of cumulative price symmetry p i0 p i i0 H 0 : i is rejected, the total oil price changes have asymmetric effect on inflation rates. In addition, we are also able to realize whether responses of inflation to oil price increase differ from that of oil price decrease using the asymmetrical test. 8

4. Estimated Results 4.1. The Data In this section we examine the oil price-inflation relationship, by means of estimating the impact of oil price changes on consumer price indexes for 29 countries across different periods. We consider monthly data of the average price of crude oil (OILP) together with the consumer price indexes (CPI), interest rates (R), and exchange rates (ER). The data for Taiwan are taken form AREMOS. All the other data are obtained from International Financial Statistics (IFS), published by the International Monetary Fund (IMF). Real oil prices are defined as the U.S. dollar prices of average crude oil deflated by the domestic (local) consumer price index. Real oil prices and CPI are measured in logarithms. Due to the data availability, the length of data in each country is different. USA has the longest data span (1957.1~2008.4 for 616 data points). Chile has the shortest data span (1977.1~2007.12 for 372 data points). Sample countries and their data periods are provided in Table 1. The estimation procedure is as follows. The first step is to verify the order of integration of each variable. In the second step, we test for multivariate cointegration among oil prices, consumer price indexes, interest rates, and exchange rates to analyze whether a long-run relation exists in our model. Third, by using Hansen (1992) test, we test for the stability of oil price change parameters. According to this result, we delineate the unstable oil price change and partition it into oil price increase and oil price decrease. Based on cointegration tests and parameter stability tests, we can correctly specify the estimated model. Fourth, we check for Granger causality to study the link between oil price changes and inflation. Finally, we test the asymmetry in terms of the relationship between oil price increase and decrease as well as inflation, and discuss our results. 4.2. Stationarity and Cointegration In order to arrive at the proper specification of the empirical model, as an important step, unit root tests need to be carried out for all of the variables. We apply Phillips and Perron (PP, 1988) unit root test to check for stationarity. The test results reported in Appendix Table 1 clearly indicate that 9

our variables are of integrated of order one (I(1)), i.e., the variables are stationary after taking first differences in all countries. Further, a necessary condition for cointegration is the integration of the series. As all the variables exhibit a unit root, we need to examine the existence of a cointegration relation. In the second step, therefore, we apply the trace and maximum-eigenvalue methods proposed by Johansen (1988) to test the long-run relation among these I(1) variables. According to Johansen s cointegration test results in Appendix Tables 2-1 to 2-4, we determine which model Autoregressive (AR) Model or Error Correction Model (ECM) is appropriate for the analysis. The third column in Appendix Table 2 displays the estimated trace statistic; the fifth column shows the maximum eigenvalues and followed by its related statistic; the last column lists the model selected to estimate according to the cointegration test results. Since the trace statistic and maximum eigenvalues reject the null hypothesis at less than the 5% significance level, it implies that there exist cointegration relations among variables. In this regard, an ECM will be chosen for analysis. The results for the whole sample in Appendix Table 2-1 show that only Côte d'ivoire and Nigeria lack a long-run relationship among variables, thus an AR model is used for these two countries. The remaining 27 countries exhibit a cointegration relation, therefore an ECM is suitable for the statistical analysis. The results for Period I (the duration before December 1986) in Appendix Table 2-2 indicate that Denmark and Nigeria do not have cointegraion relation and an AR model is applied. The other 27 countries exhibit cointegration relation and as such the ECM is used. Likewise, results for Period II (from January 1987 to December 1998) in Appendix Table 2-3 suggest that Austria, Colombia, Côte d Ivoire, Japan, Nigeria, Portugal, USA, and Venezuela do not exhibit a cointegration relation, thus the AR model is sufficient for analysis, while the ECM is used in the remaining 21 countries. In the same way, as displayed in Appendix Table 2-4 for Period III (the duration after January 1999), variables in Canada, Finland, Japan, Korea, U.K., and Venezuela are not cointegrated and an AR model is used, whereas the ECM is applied in the remaining 23 countries for the statistical analysis. 10

In sum, the general result of this analysis is that a long-run relationship in our four-variable model is evident for most of the countries both in the case of full sample and three sub-periods. 4.3. Hansen s Stability Tests In this section, we apply Mork (1989) and Mork et al. (1994) and use real oil price increases and decreases as separation variables from the parameter stability test results. To assess the stability of parameter estimates, Hansen s (1992) stability test can be utilized to determine whether the consumer price indexes respond asymmetrically or symmetrically to oil price movements. An advantage of this test is that it does not require selecting potential structural break points. Moreover, no special treatment of lagged dependent variables is required (Hansen, 1992), but the test requires variables to be stationary. The Hansen stability test produces two types of statistics: a joint test statistic and an individual test statistic. Individual test statistic represents the stability of each parameter in the equation, while the joint test assesses the stability of all the parameters jointly in the entire equation. Unlike the previous papers, we do not employ positive and negative oil price change as separation variables arbitrarily in the model. Rather, it is determined by the test results. To test the asymmetric transmission mechanism from oil prices to inflation in the four-variable model, we opt for Hansen s individual test statistics. We do so because oil price increases may or may not have different impacts on inflation compared with oil price decreases. The results are provided in Appendix Table 3-1 for the entire sample and in Appendix Tables 3-2 to 3-4 for three sub-periods. To complete the parameter stability test, we first set the maximum lag periods at 12 and determine the optimal lag length in equation by minimizing the AIC. Next, the null hypothesis of stable estimates is rejected if the individual test statistics are significant. The period of a statistically significant lag variable is used as demarcation point(s) for asymmetry. So we select the significant estimates of real oil prices as asymmetric variables. To save space, only the estimates of real oil prices are displayed in Appendix Table 3. The results in Appendix Table 3-1 for the entire sample report that individual parameters of 11

real oil price in Germany, Mexico, Netherlands, South Africa, Sweden, Trinidad & Tobago, and Venezuela are stable (no significant lag variable) and thus linear specification for oil price change is adequate for these 7 countries. In contrast, the unstable estimates found in the remaining 22 countries can be examined asymmetrically via the Hansen test. Further, the test results for Period I (pre-1986.12) are displayed in Appendix Table 3-2, in which the significant estimates of individual parameters are found for France, Japan, Malaysia, Portugal, Spain, and the U.K., and it is reasonable to separate the oil price changes into positive and negative regimes in these countries. Note that failing to reject the null of stable coefficients implies that a linear specification may be appropriate for the other 23 countries. Moreover, the results obtained from Appendix Table 3-3 for Period II (1987.1~1998.12) reveal that parameters instability is evident in Austria, Côte d Ivoire, U.K., Venezuela, and Taiwan. That is, the asymmetrical relationship between real oil price and inflation exists in these 5 countries, while a symmetric model is applied in the remaining 24 countries. In addition, the results for Period III (post-1999.1) are shown in Appendix Table 3-4. Again, the rejection of the null of parameter stability implies that there exists asymmetric oil price shock on inflation in Austria, Belgium, Chile, Colombia, France, Italy, Japan, Luxembourg, South Africa, Spain, Sweden, Trinidad & Tobago, and USA, while a symmetric relation is modeled in the other 16 countries. To summarize the stability test results, the oil price changes are found to be asymmetrical in 22 out of 29 countries over the entire sample. However, when we divide the whole sample into three sub-periods, a linear model for oil price changes is found in a majority of countries for Period I and Period II. Besides, such a significant asymmetrical relationship from oil price changes to inflation rates is found in 13 out of 29 countries in Period III. Apparently, the possibility of an asymmetry in the responses of inflation to oil price shocks is observed in Period III much more frequently than the other two periods. Since oil prices move up speedily with the velocity after 1999, but it is rare before then. Use of the separation variable such as the oil price increases could well enhance the explanatory power on inflation especially in the era when oil prices have climbed up substantially over the recent years. Thus, analysis in Period III indicates that the oil price-inflation relationship 12

can better be explained by taking price asymmetry into consideration. 4.4. Estimated Models Before estimating equations, some preliminary tests to determine the appropriate form of the empirical models must be made. Technically, some well-known statistical properties, the integration, the long-run (cointegration) relationship, and the number of lags, ought to be determined. Equally important, the stability of oil price parameters in each equation must be tested as well. Finally, the estimated equations are then specified from the test results previously carried out in the earlier sections 4.2 and 4.3. From the results in Appendix Table 2 and Appendix Table 3, cointegration and asymmetrical relationship between oil prices and inflation are determined. As stated in section 3, therefore, four main models are specified as shown in equation (1) through equation (4). The results of the estimated models are reported in Appendix Table 4, only coefficients of real oil price changes are shown for analysis. The current and lagged coefficients are displayed in columns 4 to 11. Coefficients with positive (+) or negative (-) sign represent an asymmetrical relationship between oil prices and inflation rates, or a linear relationship otherwise. In addition, the last three columns in Appendix Table 4 show total symmetrical or asymmetrical impact of oil price changes on inflation rates. Note that in Appendix Table 4, all the current and delayed coefficients (both significant and insignificant parameters) of the real oil price changes are reported in order to illustrate the contemporaneous and cumulative impact of oil prices on inflation. For the other variables, coefficients with t-statistics less than one are discarded gradually in estimation procedures according to the parsimonious principle. From the estimated results in Appendix Table 4, it is evident that the current responses of inflation rates to oil price changes are larger than the responses during the lagged periods for about one half of the countries. For instance, the current impacts of oil price change on inflation rates are greater than that in delayed periods for 15 out of 29 countries over the entire sample (13 out of 15 countries exhibit the asymmetrical relationship); the current responses of inflation to oil price changes are larger than the responses during the lagged periods for 13 countries in Period I (five out 13

of 13 countries exhibit the asymmetrical relationship); the inflation rates in response to the current oil price changes are larger than that to lagged oil price changes for 13 countries in Period II (four out of 13 countries exhibit the asymmetrical relationship); and the oil price transmissions are higher in current period than that in delayed period for 17 countries in Period III (ten out of 17 countries exhibit the asymmetrical relationship). As is to be expected, two important consequences of our estimated results are obtained. First, as summarized in Table 2, the relationship between oil price changes and inflation rates in 20 out of 27 countries is asymmetrical over the entire sample period, while a majority of them is linear in each sub-period. A possible explanation for this fact may be attributed to structural breaks detected in oil price time series. That is to say, after dividing the time series properly according to important events in the world oil market, a linear model seems to be more acceptable in each sub-period. Without a doubt, this result not only helps justify the partition of our data set into three independent periods but also makes sense for our analysis. Second, as shown in Table 3, responses of inflation in Period I are greater than that in Period II more than half of the countries. Additionally, it is observed that coefficients in Period I are larger than that in Period III for half of the countries. In other words, the magnitude of oil price transmission is greater in the 1970s-1980s than that in the recent twenty years. Well known in political arena, the global economy has experienced two oil crises (the 1973/74 Arab oil embargo and the 1978/79 Iranian revolution) and their dramatic influence of oil price changes on inflation was unprecedented. The result is in agreement with that by Blanchard and Galí (2007) and De Gregorio et al. (2007): a decline in degrees of transmission from oil price shocks to inflation was particularly evident in the 2000s. 4.5. Causality Tests An important issue in estimating model is to determine whether movements in one variable 14

are caused by movements in another. We apply the Granger (1969) model 2 to test short-run reactions from oil price changes to inflation rates based on the four-variable model as specified in equation (1) and (2), in the absent of a cointegration relation. Failing to reject the null hypothesis H 0 : 1 2 p 0 3 for the symmetrical model or failing to reject the null hypothesis H 0 : 1 1 2 2 p p 0 for asymmetrical model implies that oil price changes do not Granger-cause inflation. On the other hand, if cointegration relationship exists in the model, an error correction term is required in testing Granger causality as shown in equation (3) and (4), where denotes the speed of adjustment. Failing to reject the null hypothesis H 0 : 1 2 p 0 and 0 for symmetrical model or failing to reject the null hypothesis H 0 and 0 for asymmetrical model implies that 0 : 1 1 2 2 p p oil price changes do not Granger-cause inflation. Table 4 shows the results of these Granger causality tests. According to the results from Table 4, we find evidence of Granger causality from oil price shocks to inflation rates in almost all countries. Needless to say, a significant causality is found in 27 out of 29 countries (except Côte d Ivoire and Nigeria) over the entire period. In a similar vein, oil price changes Granger-cause inflation in 28 countries (except Denmark) for the period before 1986; evidence of oil price changes causes inflation is obtained in 21 countries (except Austria, Colombia, Côte d Ivoire, Germany, Japan, Nigeria, Portugal, and Venezuela) for the period during 1987 and 1998; and a significant causality from oil price shocks to inflation is observed in 27 countries (except Norway and Venezuela) for the period after 1999. Based on the Granger s test results, the causality from oil price changes to inflation rates is more significant when a cointegration relationship is included. Moreover, our results show that oil price changes cause inflation rates even when a linear relationship is considered. It is apparent that 2 The basic idea of Granger causality theory is to test the null hypothesis that changes in one variable are not able to predict the other. 3 The feasibility of the Granger causality tests depends on the stationarity features of the system. If the series are stationary, the null hypothesis of no Granger causality can be tested by standard Wald tests (Lütkepohl, 1991). 15

the impacts of oil price changes on inflation rates are nearly inevitable around the world. 4.6. Testing for Asymmetric Effects The results of the causality tests are used in evaluating the direction from oil price changes to inflation. Given the estimated equations (1) and (3), we can perform the test of asymmetrical responses of inflation rates to oil price increases and decreases. As observed in Table 5, we display the results obtained from analyzing current responses of inflation to positive and negative oil price changes. The current response of inflation to oil price changes is asymmetric for 11 out of 22 countries over the whole sample (with greater magnitude of transmission for oil price increases comparing with oil price decreases in Korea and Trinidad & Tobago), yet it almost disappeared in the three sub-periods. That is, the asymmetrical link from oil price shocks to inflation respectively appeared only for four countries in the first period, for three countries in the second period, and for four countries in the last period (with greater magnitude of transmission for oil price increases comparing with oil price decreases in Spain). As mentioned earlier in Section 3, testing the null hypothesis for all coefficients is equivalent to jointly testing the following two hypotheses and i i 0 0. The result presented in Table 6 refers to as the total asymmetry, tested by cumulating coefficients for both current and all lags of oil price increases and oil price decreases. Results suggest that there exist cumulative asymmetric responses of inflation to oil price increase and decrease in 12 out of 22 countries over the whole sample. These results show that the magnitude of the transmission for oil price increases are higher than that for oil price decrease in five countries, namely Côte d Ivoire, France, Italy, Japan, and Taiwan. On the contrary, the asymmetric responses have nearly vanished in all of the three sub-periods, i.e. only five countries in Period I (with higher degree of transmission for oil price increases in Malaysia), four countries in Period II (with higher degree of transmission for oil price increases in Côte d Ivoire and the U.K.), and two countries in Period III (also with higher degree of transmission for oil price increases in South Africa and Trinidad & Tobago). 16

5. Conclusion A vast volume of past research has examined the macroeonomic response to oil price shocks with a particular emphasis on real economic activity. Relatively few analyses have tackled the related question of the effect of oil prices on inflation rates. The purpose of this paper is to examine the relationship between real oil price changes and the inflation in the framework of Mork s (1989) asymmetrical model. To provide better insight into the transmission mechanism, we divide the data into three periods on the basis of major historical events in the world oil market. This analysis has supported several conclusions as follows: First of all, evidence from the analysis supports the existence long-run asymmetric responses of inflation (entire sample) to real oil price increases and decreases for a majority of countries. However, the impacts of real oil price changes on inflation are nearly linear in all the three sub-periods. A possible explanation for this outcome may emanate from a lack of structural breaks taken place in sub-periods. In other words, after dividing the time series according to important events, a linear model is much more acceptable for each sub-period. Needless to say, this outcome further gives us a convincing argument for the division of our sample period. Second, results of the oil price transmission mainly suggest that current responses of inflation to real oil price changes are larger than that in the lagged periods. Generally, the cumulative impact of real oil price increase is greater than the cumulative impact of real oil price decrease. Third, results from the Granger causality indicate that the direction from real oil price changes to the inflation is almost completely predictable in both asymmetrical and linear case. In addition, test results of the Granger causality are most statistically significant when a cointegration relationship is included in the model. Finally, by separating the data into three periods based on the important events, the oil price transmission is typically higher in Period I than that in both Period II and III: a result in line with the empirical observations that the oil price transmission is more profound in the 1970s-1980s than in the 1990s and 2000s. As is well known, the global economy experienced two major oil crises in the 1970s and 1980s that ushered in significant inflation. Furthermore, the recent oil price hike is 17

mainly the result of robust world economy and thus the degree of oil price transmission into inflation is relatively low and very stable in the last decade. Our finding is also consistent with the result of Blanchard and Galí (2007) and De Gregorio et al. (2007) that a decline in pass-through from oil price shocks to inflation has became more evident in the last ten years. 18

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Table 1 Sample Countries and Data Periods Country Data period Number of Observations (T) Austria 1971:01~2008:02 446 Belgium 1957:01~2008:02 614 Canada 1957:01~2008:03 615 Chile 1977:01~2007:12 372 Colombia 1964:01~2008:04 532 Côte d Ivoire 1964:01~2008:02 530 Denmark 1967:01~2008:03 495 Finland 1957:01~2008:01 613 France 1957:01~2008:02 614 Germany 1960:01~2008:02 578 India 1957:01~2008:03 615 Italy 1971:01~2008:02 446 Japan 1957:01~2008:03 615 Korea 1970:01~2008:02 458 Luxembourg 1970:01~2008:02 458 Malaysia 1971:01~2008:03 447 Mexico 1960:01~2008:01 577 Netherlands 1964:11~2008:02 520 Nigeria 1964:01~2008:03 531 Norway 1971:08~2008:03 440 Portugal 1966:12~2008:01 494 South Africa 1960:01~2008:03 579 Spain 1974:01~2008:02 410 Sweden 1957:01~2008:03 615 Trinidad & Tobago 1964:12~2008:03 520 United Kingdom 1964:01~2008:03 531 USA 1957:01~2008:04 616 Venezuela 1964:01~2008:01 529 Taiwan 1970:12~2008:03 448 22

Table 2 Total Effects of Inflation Rates to Symmetric and Asymmetric Oil Price Shocks Symmetrical Impacted Countries Ratio of Ratio of Total Impacted Symmetrical Asymmetry Impacted Countries Asymmetrical Countries relative to Total relative to Total Austria, Belgium, Canada, Colombia, Germany, Mexico, Netherlands, South Côte d Ivoire, Denmark, Finland, Full Africa, Sweden, Trinidad & Tobago, 7/27 France, India, Italy, Japan, Korea, Sample Venezuela Luxembourg, Malaysia, Nigeria, 20/27 27 Norway, Portugal, UK, USA, Taiwan Belgium, Canada, Chile, Côte d Ivoire, Finland, Germany, India, Italy, Korea, Period I Luxembourg, Malaysia, Netherlands, 19/24 France, Japan, Portugal, Spain, UK 5/24 24 Nigeria, South Africa, Sweden, Trinidad & Tobago, USA, Venezuela, Taiwan Belgium, Chile, Denmark, France, Period II India, Italy, Luxembourg, Malaysia, Austria, Côte d Ivoire, UK, Mexico, Nigeria, Norway, South Africa, 14/19 Venezuela, Taiwan Sweden, USA 5/19 19 Canada, Denmark, Finland, Germany, Austria, Belgium, Chile, France, Italy, Period III India, Korea, Mexico, Netherlands 13/24 Luxembourg, South Africa, Spain, 11/24 24 Nigeria, Norway, Portugal, UK, Taiwan Sweden, Trinidad &Tobago, USA Note: Cumulative responses of inflation to oil price changes are taken into consideration. 23

Table 3 Comparing the Magnitudes of Inflation Responses to Oil Shocks across Three Periods Period I Period II Period III Austria S L M Belgium S M L Canada S M L Chile S M L Colombia L S M Côte d Ivoire L M S Denmark S M L Finland S M L France M S L Germany S M L India M S L Italy L S M Japan L S M Korea L S M Luxembourg L M S Malaysia L M S Mexico S M L Netherlands L M S Nigeria S L M Norway S M L Portugal L S M South Africa S M L Spain L S M Sweden S L M Trinidad & Tobago M S L UK L S M USA M S L Venezuela L M S Taiwan L M S Summary Period I>II: 17/29 Period II>III: 9/29 Period I>III: 13/29 Note: L, M, and S represent respectively the largest, the medium, and the smallest response of inflation rates to oil price shocks. In comparison with period I, II, and III, the measures in the table are taken based on the size of cumulative effects. 24

Table 4 Granger Causality Tests from Oil Price Changes to Inflation Rates Null hypothesis: All Period I Period II Period III lroilp lcpi M Asy. Statistics p-value M Asy. Statistics p-value M Asy. Statistics p-value M Asy. Statistics p-value Austria E Y 12.504*** (0.000) E N 31.361*** (0.000) A Y 1.305 (0.275) E Y 4.902*** (0.003) Belgium E Y 4.114*** (0.001) E N 10.117*** (0.000) E N 17.404*** (0.000) E Y 2.964** (0.036) Canada E Y 37.395*** (0.000) E N 21.396*** (0.000) E N 35.514*** (0.000) A Y 9.344*** (0.000) Chile E Y 29.969*** (0.000) E N 7.366*** (0.001) E N 19.433*** (0.000) E Y 7.116*** (0.000) Colombia E Y 6.302*** (0.000) E N 32.138*** (0.000) A N 0.672 (0.414) E Y 5.288*** (0.002) Côte d Ivoire A Y 2.142 (0.119) E N 17.928*** (0.000) A Y 1.328 (0.263) E N 8.206*** (0.001) Denmark E Y 15.526*** (0.000) A N 0.000 (0.989) E N 8.594*** (0.000) E N 7.314*** (0.001) Finland E Y 4.419*** (0.001) E N 24.062*** (0.000) E N 22.917*** (0.000) A N 5.703** (0.019) France E Y 6.224*** (0.000) E Y 22.984*** (0.000) E N 28.761*** (0.000) E Y 5.363*** (0.000) Germany E N 13.600*** (0.000) E N 29.036*** (0.000) E N 0.244 (0.784) E N 9.973*** (0.000) India E Y 12.507*** (0.000) E N 9.256*** (0.000) E N 5.982*** (0.003) E N 9.090*** (0.000) Italy E Y 23.663*** (0.000) E N 43.143*** (0.000) E N 28.822*** (0.000) E Y 9.571*** (0.000) Japan E Y 5.006*** (0.002) E Y 14.188*** (0.000) A N 0.194 (0.941) A Y 2.398* (0.096) Korea E Y 22.281*** (0.000) E N 13.441*** (0.000) E N 19.647*** (0.000) A N 4.103** (0.045) Luxembourg E Y 8.170*** (0.000) E N 16.261*** (0.000) E N 38.837*** (0.000) E Y 4.594*** (0.001) Malaysia E Y 6.741*** (0.000) E Y 12.784*** (0.000) E N 14.235*** (0.000) E N 6.574*** (0.002) Mexico E N 24.901*** (0.000) E N 2.435** (0.015) E N 11.051*** (0.000) E N 10.173*** (0.000) Netherlands E N 19.803*** (0.000) E N 23.121*** (0.000) E N 2.617** (0.038) E N 2.606** (0.041) Nigeria A Y 0.125 (0.883) A N 4.882** (0.028) A N 0.315 (0.576) E N 2.132 (0.125) Norway E Y 6.968*** (0.000) E N 59.607*** (0.000) E N 10.252*** (0.000) E N 2.520* (0.086) Portugal E Y 44.242*** (0.000) E Y 21.596*** (0.000) A N 0.381 (0.538) E N 28.015*** (0.000) South Africa E N 51.609*** (0.000) E N 50.746*** (0.000) E N 20.108*** (0.000) E Y 12.632*** (0.000) 25

Spain E Y 5.212*** (0.000) E Y 35.110*** (0.000) E N 10.242*** (0.000) E Y 6.664*** (0.000) Sweden E N 14.982*** (0.000) E N 81.232*** (0.000) E N 22.879*** (0.000) E Y 2.484* (0.065) Trinidad & Tobago E N 3.547** (0.015) E N 6.946*** (0.000) E N 31.960*** (0.000) E Y 3.966** (0.010) UK E Y 5.293*** (0.000) E N 16.560*** (0.000) E N 12.122*** (0.000) A N 10.181*** (0.002) USA E Y 5.910*** (0.000) E N 15.677*** (0.000) A Y 11.788*** (0.000) E Y 3.809** (0.012) Venezuela E N 13.551*** (0.000) E N 36.956*** (0.000) A Y 0.449 (0.639) A N 0.875 (0.352) Taiwan E Y 6.238*** (0.000) E N 8.179*** (0.000) E Y 5.155*** (0.002) E N 9.824*** (0.000) 2 Note: The null hypothesis that lag values of real oil price changes do not Granger-cause inflation is tested. Numbers in the table are -statistics and the corresponding p-values are in the parentheses. *, **, and *** indicate statistically significant at 10%, 5%, and 1% level, respectively. M=A denotes AR model while M=E denotes ECM. Asy.=Y stands for asymmetrical impact of oil price changes on inflation rates while Asy.=N stands for symmetrical impact of oil price changes on inflation rates. 26