The effects of oil prices on the price indices in Taiwan: International or domestic oil prices matter?

Energy Policy 45 (2012) 730–738

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The effects of oil prices on the price indices in Taiwan: International or domestic oil prices matter? Wen-Hsiu Huang a,n, Ming-Che Chao b a b

Department of Public Finance, Ling Tung University, No.1, Ling Tung Rd, Taichung 40852, Taiwan Institute of Civil Engineering, National Chi Nan University, No.1, University Rd, Puli, Nantou County 54561, Taiwan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 July 2011 Accepted 14 March 2012 Available online 1 April 2012

This study employs the multivariate threshold model developed by Tsay (1998) to examine the effects of international and domestic oil prices on the price indices in Taiwan using monthly data from January 1999 to December 2011. The results show that changes in domestic oil prices do not Granger cause changes in the price indices when the oil price change exceeds the threshold level. However, international oil price shocks still affect the wholesale price index through the effects of announcement and expectation. Therefore, controlling domestic oil prices to prevent volatility in the price indices is ineffective. If we focus on the period when the left-wing party is in power, we also find that changes in international oil prices have more critical effects on the price indices than changes in domestic oil prices. However, the lead-lag relationship between changes in domestic oil prices and changes in the price indices vanishes except with respect to the transportation category where the consumer price index is affected by domestic oil prices. This result may be due to the government’s extensive intervention in setting oil prices. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Oil price Price index Multivariate threshold models

1. Introduction For the past decade, changes in crude oil prices have often been highlighted in the news. Crude oil prices may act as triggers that influence the aspects of both production and consumption. The effects of oil prices on economic activities have drawn the attention of researchers in recent years with one of the most important issues being the mechanism of oil price transmission. Current discussions regarding the mechanism of oil price transmission include three areas. First, many studies analyze how crude oil prices are passed on to the prices of petroleum products (Bacon, 1991; Shin, 1994; Borenstein et al., 1997; Galeotti et al., 2003; Chen et al., 2005; Grasso and Manera, 2007; Honarvar, 2009). Researchers find that prices tend to rise faster than they fall. Numerous studies attempt to explain and confirm that price transmission is asymmetric, a phenomenon referred to as ‘‘rockets and feathers’’ (Bacon, 1991; Borenstein et al., 1997; Johnson, 2002; Lewis, 2004). The second stream of research regarding oil price transmission concentrates on analyzing the relationship between oil prices and the prices of specific commodities (Baffes, 2007; Soytas et al., 2009; Sari et al., 2010; Nazlioglu, 2011; Nazlioglu and Soytas, 2011). Researchers find

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that there are relationships between oil prices and other commodity prices and attempt to explain why the raw commodities have a persistent tendency to move together (Pindyck and Rotemberg, 1990; Lescaroux, 2009). The third area analyze the effects of oil prices on inflation or price levels through a macroeconomic view (Cologni and Manera, 2008; Tang et al., 2010; Du et al., 2010; Chen, 2009; Chou and Tseng, 2011). Studies in the literature have examined the impact of oil prices on the GDP or economic growth, and the pass-through effects of oil price on inflation or price levels. All of the above studies consider the effects of international oil prices on inflation or price levels, but ignore the effects of domestic oil prices. In fact, the literature shows that, on the one hand, international oil prices have effects on domestic oil prices, then domestic oil prices pass on to inflation or price levels. On the other hand, international oil prices may affect the price level and inflation through the comovement of raw material prices and the effects of announcement and expectation. Thus, in this paper, both the impacts of international and domestic oil prices on the price indices would be considered. In Taiwan, approximately 99% of the energy supply depends on imported energy, and the economy is highly energy intensive. In 2008, energy productivity per capita in Taiwan was 3.78 US$1000 per ton of oil equivalent, lower than that of the UK (12.83), Italy (13.12), Germany (10.9), Japan (9.87), and the U.S. (6.29). Of all forms of energy, oil consumption constitutes the greatest share of

W.-H. Huang, M.-C. Chao / Energy Policy 45 (2012) 730–738

energy consumption in Taiwan. For example, in 2010, of the energy consumed in Taiwan, 45% was in the form of oil. Almost 100% of the crude oil supply is imported. Therefore, Taiwan’s economy suffers from imported inflationary pressure due to its high dependence on imported oil. Chou and Tseng (2011) verified that the long-term pass-though effect of international oil prices on inflation in Taiwan was significant. The domestic oil market in Taiwan was a regulated industry and monopolized by state-owned oil company — Chinese Petroleum Corporation (CPC) before 2000. The Formosa Petrochemical Corporation (FPCC), a privately-owned oil company established in 2000. About 90% of refined oil products are produced by them during 2000 to 2010. CPC was forced to follow governmental policy for stabilizing prices. Although FPCC could adjust oil prices based on the market mechanism, FPCC was forced to readjusted back to the same price as CPC accounting for its market share (Wu et al., 2011). Moreover, the companies must obey the request of government such as the price freezing strategies in past few years. Thus, the domestic oil prices were controlled by the government. Since September 2006, Taiwan began to implement the floating oil price mechanism that adjusted domestic oil price based on the imported cost change in a certain period. The gasoline and diesel prices returned to market mechanism. However, the government still often intervenes in setting the prices of petroleum products to prevent international oil shocks. This price setting was especially apparent during the period from December 2007 to May 2008 when the government set a ceiling for prices of petroleum products. Domestic oil prices adjust more slowly relative to international oil prices. This phenomenon raises the following questions: ‘‘Is it really effective to stabilize the price level by controlling domestic oil prices?’’ and ‘‘Do crude oil prices or domestic petroleum prices play a critical role in the process of price transmission?’’ We are also interested in investigating these questions. In this paper, we employ the multivariate threshold model developed by Tsay (1998) to examine the effects of international and domestic oil prices on the price indices in Taiwan using monthly data from January 1999 to December 2011. In addition, some studies suggest that political ideology of incumbent governments affects the relationship of the variables in which we are interested (Bjørnskov, 2008; Potrafke, 2009). As for the price policy, the right-wing parties may stand for a laissez-faire economy while the left-wing parties lean to a regulated policy. Thus, when the party in power changes, does the relationship between oil prices and the price indices change as well? It would be beneficial to explore these questions, especially as oil prices have become such an important indicator in the world. Therefore, we attempt to answer these questions in this paper. The empirical results indicate that changes in the price indices are not Granger caused by changes in domestic oil prices when the oil price changes exceed the threshold level. Although the effects of domestic oil prices on the price indices are not significant when oil price changes are large, international oil prices have substantial effects on the price indices. International oil price shocks may affect the price indices through the effects of announcement and expectation. Thus, controlling domestic oil prices to prevent volatility in the price indices is ineffective. Furthermore, as the lead indictors in the process of price transmission, international oil prices are more important than domestic oil prices. Moreover, if we focus on the period when the left-wing party is in power, we find that changes in international oil prices Granger cause the wholesale price indices. However, the lead– lag relationships between changes in domestic oil prices and changes in the CPI and WPI vanishes with the exception of the CPI in the transportation category, which is affected by changes in domestic oil prices. Because of the government’s extensive

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intervention in setting oil prices, the direct price transmission from domestic oil prices to the price indices is eliminated. The policy implication is that to prevent volatility in the price indices by intervening with domestic oil prices is useless, because changes in prices indices are still affected by changes in international oil prices. To prevent oil shocks, the government should put emphasis on building people’s confidence in the price stability and avoid people’s excessive expectations. The structure for the remainder of the paper is as follow. Section 2 describes the multivariate threshold model and presents the data. Section 3 reports our empirical findings. Section 4 draws the conclusions and describes the policy implications of the results.

2. Methodology and data 2.1. Multivariate threshold auto-regression model A nonlinear time-series analysis is an important development in recent studies as it can correct for biased estimations in linear assumptions and capture asymmetric and nonlinear characteristics of certain economic phenomena. Based on the concept of nonlinear analysis, various empirical methods have been developed in the studies, such as Hansen (1992, 1996), Enders and Granger (1998), Enders and Siklos (2001), and Hansen and Seo (2002). In this paper, we use the multivariate threshold autoregression model (TAR) suggested by Tsay (1998) to investigate the nonlinear relationship between oil prices and the price indices in Taiwan. Tsay (1998) generalized Tsay (1989) univariate threshold auto-regression model to a multivariate context. The application of the TAR model usually can be divided into two steps. The first step is to test for nonlinearity, that is, to detect whether different regimes exist that are determined on the basis of the threshold variable. The second step is to estimate, using a TAR model, whether the threshold effects exist. Consider a k-dimensional time series yt ¼(y1t, y, ykt)0 and v-dimensional exogenous variables xt ¼(x1t, y, xvt)0 . The multivariate TAR model with threshold variable zt and delay d can be expressed as the following: yt ¼ cj þ

p X

aji yti þ

i¼1

q X

bji xti þ ejt if r j1 o ztd r rj

ð1Þ

i¼1

The threshold parameters satisfy the constraint  N¼ r0 or1 o  ors  1 ors ¼ N, where j ¼1, y, s. The threshold variable zt  d is assumed to be stationary and to have a continuous distribution; cj are constant vectors; p and q are non-negative P1=2 P1=2 integers. The innovations satisfy ejt ¼ j at , where are j symmetric positive definite matrices, and {at} is a sequence of serially noncorrelated random vectors with mean zero and covariance matrix I, the identity matrix. We assume that p, q, and d are known and sample size is n. For the purpose of detecting the threshold nonlinearity of the model, we can use the least squares method and express the model in the following form: y0t ¼ X 0t F þ e0t ,

t ¼ h þ 1,. . .,n

ð2Þ y0t1 ,. . .,y0tp ,x0t1 ,. . .,x0tq Þ0

is a where h¼max(p, q, d), X t ¼ ð1, (pk þqvþ 1) dimensional regressor and F is the parameter matrix. The threshold variable z  d assumes values in S ¼{zh þ 1  d,y,zn  d}. Let (i) be the time index of the ith smallest observation in S. Then, we can express the arranged regression according to the ascending order of the threshold variable z  d as: y0tðiÞ þ d ¼ X 0tðiÞ þ d F þ e0tðiÞ þ d

i ¼ m, m þ1,. . .,nh,

ð3Þ

where m is the number of startup observations in the order autopffiffiffi regression. Tsay (1998) suggests that a suitable value of m is 3 n

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pffiffiffi for the stationary case and 5 n for the non-stationary case. From the above least squares multivariate regression, we obtain the one-step-ahead standardized predictive residual e^ tðm þ 1Þ þ d . If yt follows a threshold model, the predictive residuals are not white noise, and they are correlated with the regressor Xt(i) þ d. On the other hand, if the model is linear, the predictive residuals are white noise and are not correlated to the regressor. Therefore, we can consider the following regression: e^ tðlÞ þ d ¼ X 0ðlÞ þ d b þ w0ðlÞ þ d

l ¼ m þ 1,. . .,nh,

ð4Þ w0tðlÞ þ d

is the where b is the matrix regression parameter and matrix of residuals. The nonlinearity test, then, is to test the hypothesis H0:b ¼0. When the null hypothesis can be rejected, nonlinearity can be supported. However, if the null hypothesis cannot be rejected, then the model is a linear process. The test statistic is CðdÞ ¼ ½nhmðpk þ qv þ 1Þ  fln9S0 9ln9S1 9g,

ð5Þ

where 9S09 and 9S19 denote the determinant of the matrix S0 and S1, respectively. Furthermore, S0 ¼

nh X 1 0 e^ e^ nhm l ¼ m þ 1 tðlÞ þ d tðlÞ þ d

S1 ¼

nh X 1 ^ ^0 , w w nhm l ¼ m þ 1 tðlÞ þ d tðlÞ þ d

^ t is the least square residual of Eq. (4). C(d) is asymptowhere w tically a chi-square random variable with k(pk þqvþ1) degrees of freedom. Additionally, the test results of Eq. (4) for different d can provide useful information to select the delay parameter of the threshold variable. When d is correctly specified, the test is most powerful. Before we test for nonlinearity, the values of p, q, and s should be determined in advance. As for the values of p and q, we can use the Akaike’s information criterion (AIC) to select the optimal lags in a linear vector auto-regression model. However, to specify the number of regimes is a more difficult problem. Tsay (1998) suggested that, in some cases, past experience and substantive information can help to identify or select the value of s. Because of the computational complexity and the data, one may restrict s to a small number such as 2 or 3. In this paper, because of limited data, we set s ¼2. Then, we use a grid search method for a given p, q, d, and s value to select the optimal threshold value by minimizing the AIC values. Once the threshold value is determined, the TAR model can be estimated. 2.2. Data The variables used in our model are international oil prices (POIL), domestic oil prices (WEOIL), the WPI, and the CPI. To investigate the effects of oil price on the CPI through a disaggregated view, we use different categories of consumer price indices, including food (CPIFOOD), clothing (CPICLO), housing (CPIHOU), transportation and communication (CPITRA), education and entertainment (CPIEDU), medicines and medical care (CPIMED), and miscellaneous (CPIMIS). We use Dated Brent crude oil prices to represent international oil prices.1 Accounting for the pass-through of exchange rates, 1 A more reasonable indicator is the average weighted imported crude oil prices, but the data are not available. Thus, we use Dated Brent oil prices to represent the international prices in the models. The Dated Brent oil prices can be the recognized benchmark price assessment of crude oil, because the gap between Brent and WTI prices has widened since 2010. The WTI oil prices can hardly represent international oil prices, and Dated Brent is increasingly being used to determine the value of sweet crude oil.

international oil prices are presented in terms of New Taiwan (NT) dollar per barrel. The data are obtained from the International Financial Statistics database. Domestic oil prices comprise the weighted average wholesale price of gasoline, diesel oil, and fuel oil.2 We use the consumption ratio of petroleum products as the weights. All the prices of petroleum product data come from the AREMOS database, provided by the Taiwan Economic Data Center. The price indices data are derived primarily from the Directorate-General of Budget, Accounting and Statistics (DGBAS), Taiwan. The base year of the price indices is 2006. The sample period is from January 1999 to March 2011. All variables are transformed in logarithms. In our models, the transmission path of oil shocks is that, on the one hand, international oil prices have effects on domestic oil prices, then domestic oil prices pass on to the price level. On the other hand, international oil prices may affect the price level through the co-movement of raw material prices and the effects of announcement and expectation. Thus, both the impacts of international and domestic oil prices on the price indices should be considered. The WPI and CPI are endogenous variables for their interaction and responses to oil prices. Both international and domestic oil prices are exogenous variables because the Taiwan economy is a price-taker in the international oil market, and the domestic oil market is a regulated industry. The domestic oil prices usually are determined by non-economic factors. However, to treat domestic oil prices as exogenous variables should be examined carefully. The government may want to regulate the domestic oil prices in order to suppress the inflation. Thus, the domestic oil prices may be affected by CPI and WPI. In order to confirm that our models are correctly specified, we test weak exogeneity of variables (Johansen, 1992). Table 1 reports values of the statistic for testing weak exogeneity of variables. The null hypothesis is that the given variable is weakly exogeneous. The statistic tests whether or not the corresponding adjustment coefficient is zero. If the adjustment coefficient is zero, disequilibrium in the cointegrating relationship does not feed back onto that variable. The results show that the null hypothesis of weak exogeneity can be rejected for the CPIs and WPI variables in each model. The CPIs and WPI variables are endogenous. However, the weak exogeneity of international and domestic oil prices can be not rejected. Given the results, the specification of our model is acceptable.3 We use a two-regime bivariate threshold auto-regression model. If the WPI and CPI are co-integrated, the error-correction term will be added in the model. The model that analyzes the effects of oil prices on the price indices (including the WPI and CPI) is denoted as model 1. Because we also investigate the effects of oil prices on the CPI through a disaggregated view, we replace the CPI in the model with the CPIFOOD, CPICLO, CPIHOU, CPITRA, CPIEDU, CPIMED, and CPIMIS, respectively. These models are denoted in order as model 2 to model 8.

2 In Taiwan, about 90% of refined oil products are produced by the two oil companies (CPC and FPCC). Only 10% of oil products are imported. Therefore, we would only consider the prices of petroleum products produced by domestic companies. 3 We do not consider the monetary sector in the models for the following reasons. First, our models are relative to a mark-up model that oil factors determine the price indices. The mark-up model assumes that the price level is a mark-up over domestic and import costs (de Brouwer and Ericsson, 1998). Second, the effectiveness of price stability is limited for the reasons of Taiwan’s high degree of economic openness. Taiwan could hardly curb deflation and inflation caused by external factor (Shea and Yang, 2006). In addition, Chen and Wu (2010) investigate monetary policy in Taiwan for the period from 1998 to 2008. They find that the monetary authority’s response to the inflation gap and output gap are not significant.

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Table 1 Weak exogeneity tests. Model

CPI

1 2 3 4 5 6 7 8

4.4223nn 4.4237nn 10.8592nnn 4.1968nn 4.7604nn 4.3230nn 3.1019n 7.7404nnn

WPI (0.0351) (0.0354) (0.0010) (0.0405) (0.0291) (0.0376) (0.0782) (0.0054)

WEOIL

6.4809nn 4.3178nn 6.2371nn 12.8588nnn 9.9765nnn 7.9344nnn 6.3641nn 5.0510nn

(0.0109) ( 0.0377) ( 0.0125) (0.0003) (0.0015) (0.0049) (0.0116) (0.0246)

0.0438 1.9702 2.1489 0.0003 0.3171 0.4040 0.0920 0.1852

POIL (0.8341) (0.1604) (0.1427) (0.9854) (0.5733) (0.5250) (0.7617) (0.6669)

2.2882 1.9019 2.3364 0.1886 0.1503 0.7598 0.3938 2.2905

(0.1304) (0.1679) (0.1264) (0.6641) (0.6983) (0.3834) (0.5303) (0.1302)

Notes: 1. The weak exogeneity test statistics are evaluated under the assumption that cointegration number is one and are asymptotically distributed as w2(1) if weak exogeneity of the specified variables for the cointegrating vector is valid. The value in the parenthesis is p-value. 2. The CPI variable is replaced by CPIFOOD, CPICLO, CPIHOU, CPITRA, CPIMED, CPIMED, and CPIMIS in models 2 to 8, respectively. 3. n, nn, and nnn represent 10%, 5%, and 1% significance levels, respectively.

Table 2 ADF and KSS unit root test. Variable

Level

First difference

ADF

POIL WEOIL WPI CPI CPIFOOD CPICLO CPIHOU CPITRA CPIEDU CPIMED CPIMIS

KSS

Intercept

Intercept and trend

 2.3156  0.5963  0.9779  0.7109  0.8423 0.4195  0.3311  2.2511  2.4953  1.5802  0.8250

 3.2314n  3.0054  3.0936  3.1949n  3.2400n  2.3148  0.8402  3.2270n  3.2190n  2.4756  3.2197n

ADF

KSS

Intercept

Intercept and trend

 10.8809nnn  10.1399nnn  7.0934nnn  14.6763nnn  11.0006nnn  3.3300nn  3.6914nnn  8.1481nnn  15.5423nnn  11.8949nnn  12.9918nnn

 10.9265nnn  10.1146nnn  7.0672nnn  14.6605nnn  10.9867nnn  3.6943nn  10.0817nnn  8.1341nnn  15.5719nnn  11.9292nnn  12.9836nnn

 2.6236  2.2291  1.6732  3.2126n  3.0820  3.2359n  3.0277  2.6575  2.2522  2.3948  3.2617n

 4.9871nnn  5.9913nnn  4.7313nnn  10.0314nnn  8.6554nnn  10.3642nnn  10.4971nnn  3.9576nnn  11.2158nnn  10.4681nnn  5.5095nnn

Notes: 1. n, nn, and nnn represents 10%, 5%, and 1% significance levels, respectively. 2. The asymptotic critical values for KSS test at 10%, 5%, and 1% significance levels are  3.13,  3.40, and  3.93. 3. We used de-meaned and de-trended data for the KSS test.

3. Empirical results 3.1. Unit root and linear co-integration tests We examine the stationarity for all series using the Augmented Dickey Fuller (ADF) test, and we consider two types of models in the ADF tests. One test includes just constant terms while the other includes constant and deterministic trend terms. We use the AIC as the criterion for selecting the appropriate lag number. Because the series may be nonlinear, we also use Kapetanios et al., 2003 nonlinear unit root test (KSS test) to examine the stationarity of all series. The results of the unit root tests are presented in Table 2. Both the ADF and the KSS tests indicate that all series are not significant at the 5% significance level, and these series are nonstationary in level value. Furthermore, we examine the series in first difference. The results suggest that these series are stationary at the 5% significance level. Thus, these series can be denoted as I(1). Because all variables are integrated of order one, we use the two-stage co-integration test presented by Engle and Granger (1987) to investigate the long-run relationships between the WPI and the CPIs. In the first step, we estimate the long-run relationship by using an OLS regression. Next, the residual series are obtained. In the second step, we test the stationarity of residual series using three methods: the ADF test, the Phillips–Perron (PP) test, and the KPSS test. If a residual series is stationary, co-integration can be verified. The ADF and PP tests are based on the null hypothesis that the

Table 3 Engle and Granger (1987) co-integration test. Model WPI, WPI, WPI, WPI, WPI, WPI, WPI, WPI,

CPI CPIFOOD CPICLO CPIHOU CPITRA CPIEDU CPIMED CPIMIS

ADF

PP nnn

 3.4928 a  3.6846nnna  1.9707nnna  2.3277nna  4.0885nnna  3.6925nnna  3.8148nnna  2.9940nnna

KPSS nn

 3.2700 a  3.3916nna  7.2660nnna  6.2328nnnb  3.7536nnna  3.4912nnna  3.3539nnna  2.9737nna

0.2630b 0.1276nc 0.1106c 0.1360b 0.0742b 0.1554b 0.0995c 0.2827b

Notes: 1. ‘‘a’’ indicates a model without a constant or deterministic trend. ‘‘b’’ indicates a model with a constant but without deterministic trend. ‘‘c’’ represents a model with a constant and deterministic trend. 2. n, nn, and nnn represent 10%, 5%, and 1% significance levels, respectively.

residual series is not stationary. In contrast, the KPSS test developed by Kwiatkowski et al. (1992) is based on the null hypothesis that residual series are stationary. When the ADF and PP test are used, we can consider three different models: a constant, a constant and a linear time trend, and neither in the test regression. As for performing the KPSS test, we have two choices: a constant or a constant and a linear time trend. The power of the unit root test will reduce if the model includes irrelevant regressors. Similar to the method that Lardic and Mignon (2006) and Arouri and Fouquau (2009) use, we employ the F-test to select the optimal model. The

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results of Engle and Granger’s (1987) co-integration test are reported in Table 3. The co-integration relationship can be supported at the 5% significance level under the ADF, PP, and KPSS tests.

3.2. Threshold nonlinearity test As the co-integration relationship is verified, we use the vector error correction model (VECM) to estimate the effects of oil prices on the price indices. However, accounting for the possibility of nonlinearity, we employ the threshold nonlinearity test developed by Tsay (1998) to investigate whether threshold-type nonlinearity exists. The differenced crude oil price and domestic oil price are considered as the threshold variables. We use the AIC to select the optimal lags, p. We set m¼36 and perform the C(d) test with different values of d, drp. Once the value of C(d) is significant, the null hypothesis of linearity can be rejected. When the value of C(d) is the most significant, the value of d is optimal. The results are reported in Table 4. When we employ DPOIL as the threshold variable, at the 5% significance level, the results reject the linear hypothesis in models 1, 2, 5, and 6. The values of d, the delay parameters, are 0, 2, 0, and 4, respectively. When the threshold variable is DWEOIL, the results exhibit nonlinearity in models 1, 2, 5, and 6. The values of d for these models are 0, 2, 0, and 1, respectively. When the results of threshold nonlinearity test support nonlinearity, the TAR model is employed to estimate; otherwise, the linear auto-regression model is adopted.

3.3. Estimation results Because the results of the threshold nonlinearity test reject the linear hypothesis in models 1, 2, 5, and 6, we can estimate the threshold vector error correction model with two regimes. Given p, d, and s, we use a grid search method to select the critical threshold value by minimizing the AIC. When the sample data are satisfied with the condition that the value of the threshold variable is larger than the critical threshold value, we denote it as regime I. On the contrary, it is denoted as regime II. However, in models 3, 4, 7, and 8, the linear hypothesis cannot be rejected. We use the linear vector error correction model to estimate. Tables 5 and 6 report the estimation results of the threshold model and the linear model, respectively. We use the Q-test to diagnose whether the residuals of the models are auto-correlated. The results of the Q-test indicate that the autocorrelation can be rejected in all models. We also use the Granger causality test to investigate lead-lag relationships. In the two-regime models, there are several findings obtained. First, no matter when DPOIL or DWEOIL is used as the threshold variable, all models show that error correction term coefficients are significantly different from zero in the DCPIs equations under the regime I. Additionally, the error correction term coefficients of the DCPI and DCPIFOOD equations are also significantly different from zero under regime II. It is evident that the mechanism of error correction exists in the adjustment of DCPIs, especially when changes in crude and domestic oil prices are larger than

Table 4 Results of threshold nonlinearity test. Model

p

d 0

1

2

C(d) P-value C(d) P-value C(d) P-value C(d) P-value C(d) P-value C(d) P-value C(d) P-value C(d) P-value

38.22 0.0329 33.92 0.0861 59.77 0.3404 51.13 0.6592 46.20 0.0042 39.42 0.4964 28.75 0.2296 28.94 0.2225

29.42 0.2046 27.66 0.2747 40.76 0.9372 43.80 0.8819 38.86 0.0282 28.57 0.9112 19.43 0.7286 18.28 0.7893

25.30 0.3897 36.75 0.0463 59.49 0.3496 63.88 0.2194 23.64 0.4824 46.36 0.1975 21.70 0.5971 25.82 0.3622

Threshold variable: DWEOIL 1 2 C(d) P-value 2 2 C(d) P-value 3 6 C(d) P-value 4 6 C(d) P-value 5 2 C(d) P-value 6 7 C(d) P-value 7 2 C(d) P-value 8 2 C(d) P-value

41.43 0.0150 32.76 0.1092 51.28 0.6539 64.27 0.2094 43.19 0.0095 38.74 0.5269 25.90 0.3583 23.39 0.4967

20.22 0.6840 24.06 0.4583 56.84 0.4435 48.09 0.7649 41.15 0.0161 69.42 0.0027 29.00 0.2200 15.84 0.8937

26.49 0.3287 36.74 0.0465 68.24 0.1264 66.49 0.1593 28.34 0.2459 40.77 0.4365 23.81 0.4726 26.99 0.3048

Threshold variable: DPOIL 1 2 2

2

3

6

4

6

5

2

6

4

7

2

8

2

Notes: The values of p are optimal chosen by the AIC.

3

4

5

6

53.22 0.5808 49.32 0.7239

36.79 0.9779 51.57 0.6433

53.78 0.5593 46.44 0.8149

55.16 0.5068 61.26 0.2928

26.33 0.9527

55.90 0.0487

47.28 0.7903 39.36 0.9553

54.98 0.5136 54.56 0.5297

54.77 0.5214 34.86 0.9881

37.46 0.9732 48.97 0.7357

21.37 0.9931

50.50 0.1243

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Table 5 Estimation results of threshold model. Regime

Dependent variable

Threshold variable: DPOIL Model 1: threshold value ¼  0.0497 I DCPI DWPI II DCPI DWPI Model 2: threshold value ¼  0.0222 I DCPIFOOD DWPI II DCPIFOOD DWPI Model 5: threshold value ¼0.0001 I DCPITRA DWPI II DCPITRA DWPI Model 6: threshold value ¼  0.0361 I DCPIEDU DWPI II DCPIEDU DWPI Threshold variable: DWEOIL Model 1: threshold value ¼0.0046 I DCPI DWPI II DCPI DWPI Model 2: threshold value ¼  0.0146 I DCPIFOOD DWPI II DCPIFOOD DWPI Model 5: threshold value ¼  0.0146 I DCPITRA DWPI II DCPITRA DWPI Model 6: threshold value ¼  0.0226 I DCPIEDU DWPI II DCPIEDU DWPI

Error correction term coefficient

Wald test

Q(4)

Adj R2

obs

DCPI1

DWPI

DPOIL

 0.1071nn  0.0274  0.3401nn  0.0151

– 2.7550n – 4.1147n

4.2026nn – 4.3862nn –

1.8389 10.5134nnn 0.4090 4.7286nn

0.0462 0.3062 4.4222nn 8.7184nnn

2.22 0.95 0.68 0.14

(0.69) (0.92) (0.95) (0.98)

0.35 0.44 0.32 0.86

121

 0.1780nnn  0.0019  0.1813nn  0.0509nn

– 0.0076 – 0.5433

0.8095 – 6.2620nnn –

2.0579 34.1517nn 3.0297n 13.6065nnn

2.9768n 2.9872n 0.1771 4.4214nn

1.69 4.26 0.59 0.72

(0.79) (0.37) (0.96) (0.95)

0.31 0.74 0.24 0.67

110

 0.0742nn  0.2202nnn  0.0020 0.0568

– 0.0726 – 0.0934

0.2973 – 0.9136 –

4.1644nnn 13.1341nnn 9.9228nnn 16.2206nnn

3.4809n 0.6635 33.3683nnn 9.5979nnn

2.62 0.33 0.92 0.25

(0.62) (0.98) (0.92) (0.98)

0.28 0.48 0.76 0.81

97

 0.1001nn 0.0393  0.1514n 0.0037

– 0.8892 – 0.6116

1.0730 – 0.0347 –

0.3293 12.3758nn 3.6315n 0.1555

0.3069 2.2088 0.0065 1.1941

3.80 1.21 1.47 0.74

(0.43) (0.88) (0.83) (0.95)

0.26 0.75 0.55 0.48

117

 0.0829nn  0.0391  0.1889nn  0.0037

– 1.3157 – 0.9259

5.1931nn – 0.6561 –

0.3636 4.8211nn 1.6980 26.4295nnn

1.2123 3.0312n 4.2017nn 4.2821nn

3.02 2.04 0.18 4.74

(0.55) (0.73) (0.97) (0.31)

0.26 0.48 0.27 0.80

90

 0.1182nn  0.0124  0.3393nnn 0.0390

– 0.0150 – 0.1037

0.0813 – 0.5347 –

2.4453 19.1643nnn 0.1803 18.2110nnn

0.2129 1.0636 0.0440 0.1392

5.31 2.04 0.76 0.15

(0.26) (0.73) (0.94) (0.98)

0.32 0.51 0.32 0.88

121

 0.1788nn  0.1576nn  0.1560  0.0427

– 0.0795 – 0.2343

0.1539 – 0.0397 –

24.3033nnn 14.4613nnn 8.7568nnn 7.5468nn

3.0162n 2.8940n 5.0626nn 6.6258nn

0.64 1.04 0.51 2.50

(0.95) (0.90) (0.97) (0.64)

0.30 0.53 0.73 0.86

121

 0.1241nn 0.0518  0.3034  0.0158

– 0.0765 – 0.5030

0.3358 – 2.4071 –

2.9771n 6.7462nn 0.0010 0.1181

2.3798 3.2060n 0.6892 4.2822n

0.52 1.84 0.82 1.14

(0.97) (0.76) (0.94) (0.89)

0.28 0.65 0.27 0.92

118

DWEOIL

32

39

56

33

63

32

32

33

Notes: 1. The DCPI variable is replaced by DCPIFOOD, DCPITRA, and DCPIEDU in models 2, 5, and 6, respectively. 2. The value in the Wald test column is F statistics. The value in the parenthesis of Q test is p-value. 3. n, nn, and nnn represent 10%, 5%, and 1% significance levels, respectively.

Table 6 Estimation results of the linear model. Model

Dependent variable

Error correction term coefficient

Wald test

DCPI 3 4 7 8

DCPICLO DWPI DCPIHOU DWPI DCPIMED DWPI DCPIMIS DWPI

 0.5779nnn  0.0175  0.3512nnn  0.0624  0.0702nn 0.0245  0.0841nnn 0.0220

1

– 0.2296 – 11.9173nnn – 0.0054 – 0.1229

Q(4)

DWPI

DPOIL

DWEOIL

5.7817nnn – 2.7328nn – 0.0024 – 0.7132 –

1.4473 31.3578nnn 0.5731 27.3762nnn 1.8819 33.4053nnn 0.4039 30.9265nnn

0.3497 5.9773nnn 1.3273 3.8409nn 0.0028 5.5728nnn 2.2846n 6.1979nnn

2.37 5.55 2.43 1.52 0.68 7.15 2.03 6.84

(0.67) (0.24) (0.79) (0.82) (0.95) (0.21) (0.73) (0.23)

Adj R2

Lag

0.96 0.67 0.60 0.68 0.19 0.65 0.25 0.65

5 1 5 2 1 1 3 1

Notes: 1. The DCPI variable is replaced by DCPICLO, DCPIHOU, DCPIMED, and DCPIMIS in models 3, 4, 7, and 8, respectively. 2. The value in the Wald test column is F statistics. The value in the parenthesis of Q-test is p-value. 3. n, nn, and nnn represent 10%, 5%, and 1% significance levels, respectively.

the threshold values. The negative sign of the error correction term coefficients is consistent with our expectation. For example, when the change of upstream price increases, a negative error

term occurs. Consequently, the DCPIs respond negatively to the error term and rise upward to maintain the equilibrium relationship.

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Second, when we use DPOIL and DWEOIL as the threshold values, the absolute value of the error correction term coefficient under regime II is larger than that under regime I, except for model 5. This result suggests that, in general, the adjustment speeds of the error correction mechanism are fast when the oil price changes are small and slow when the oil price changes are large. However, in the CPITRA model (model 5), the speed of the long-run adjustment in the regime with large oil price fluctuations is faster than that in the regime with moderate oil price changes. Hence, the consumer price index of the transportation category is sensitive to the oil price changes when the oil price shocks are great. Third, the results of the Granger causality test show that, at the 5% significance level, DPOIL Granger causes DWPI in models 1, 2, and 5. We also find that DPOIL Granger causes DCPI in model 5. As for the lead-lag relationship between DWEOIL and the price indices, changes domestic oil prices Granger cause DCPI and DWPI in models 1 and 5, but the relationship only exits under regime II. It is worth noting that DWEOIL does not Granger cause DCPI and DWPI when the oil price change exceeds the threshold level. This result may be because the government intervenes in the oil pricing policy. When the oil price shocks are greater, the government intervenes even more in the price setting. Although the effects of domestic oil prices on the price indices are not significant when the oil price changes are large, international oil prices still have substantial effects on the price indices. International oil price shocks may affect the prices indices through the effects of announcement and expectation or the co-movement of raw material prices. Thus, controlling domestic oil prices to prevent volatility in the price indices is ineffective. The prices of goods and services respond to international oil price shocks even when domestic oil prices have not co-moved with crude oil prices. In other words, international oil prices are more important than domestic oil prices as lead indicators in the process of price transmission. When we observe the results of linear models, the error correction term coefficients are significantly different from zero in the DCPIs equations but not in the DWPI equations. The results of the DWPI equations are the same as the threshold models. In the two-regime threshold model, the long-run adjustment effects are not significant in DWPI equations for most models. Furthermore, from the results of the Granger causality tests above, we know that the short-run effects of oil price changes on DWPI are obvious. Furthermore, we find the DWPI can Granger cause DCPIs in some models, such as models 1, 2, 3, and 4. Thus, we can construct the relationship between oil prices and the price indices as follows. Oil price shocks pass on the DWPI. Then, DCPIs may respond to the DWPI from the short-run effects, and DCPIs adjust to obtain the long-run equilibrium from the error correction mechanism. However, when oil price changes are large, the effects of domestic oil prices on the price indices vanish for government’s intervention. International oil prices become the crucial factor to the movement of price indices. Moreover, we compare the responses of different classified CPIs to international oil prices. Only the DCPITRA is Granger caused by DPOIL. As for the effects of DWEOIL on different classified CPIs, only DCPITRA is Granger caused by DWEOIL under regime II in the threshold model. This result suggests that the consumption of transportation category is more sensitive than other kinds of consumption to crude oil price shocks. Hence, the oil price shocks on prices of transportation goods and services should be given more attention. The empirical results show that the international oil prices are more important than domestic oil prices to the price indices. The government’s intervention in domestic oil price is ineffective to the price stability, because the price indices still are still influenced by international oil prices. Moreover, freezing domestic oil price to stabilize the price level goes against the aim of energy

saving. The more correct strategy is let the domestic oil prices return to market mechanisms, restrain the oil consumption, and induce the use of alternative energy. To prevent oil shocks, the government should put emphasis on building people’s confidence in the price stability, avoid people’s excessive expectations, and stop the price level increasing further more.

3.4. The period of left-wing party in power Some studies suggest that the political ideology of incumbent governments affects the relationship of the variables in which we are interested (Bjørnskov, 2008; Potrafke, 2009). As for the price policy, the right-wing parties may support for a laissez-faire economy while the left-wing parties may prefer a regulated policy. Thus, we use the subsample data to investigate the relationship between oil prices and the price indices during the period when the left-wing party is in power. The subsample period is from June 2000 to May 2008. First, we employ the threshold nonlinearity test to investigate whether threshold-type nonlinearity exists. We also use the differenced crude oil price and domestic oil price as the threshold variables. Table 7 reports the results of the threshold nonlinearity test in the subsample period. No matter when DPOIL or DWEOIL is used as threshold variables, no evidence shows that the threshold-type nonlinearity exists in any model. Therefore, using the linear vector error-correction model to estimate is suitable. Table 8 shows the results of the VECM in the subsample. At the 5% significance level, the coefficients of the error correction term are significantly different from zero in the DCPIs equations, with the Table 7 Results of threshold nonlinearity test: 2000m6–2008m5. Model p

d 0

1

Threshold variable: DPOIL 1 1 C(d) 15.49 P-value 0.4893 2 1 C(d) 12.12 P-value 0.7356 3 6 C(d) 46.58 P-value 0.8109 4 4 C(d) 41.67 P-value 0.3979 5 1 C(d) 19.59 P-value 0.2390 6 1 C(d) 19.50 P-value 0.7249 7 1 C(d) 13.30 P-value 0.6504 8 2 C(d) 19.06 P-value 0.7489

9.36 0.8977 11.83 0.7555 31.22 0.9970 35.45 0.6749 16.24 0.4360 13.42 0.9587 10.22 0.8551 31.07 0.1517

Threshold variable: DWEOIL 1 1 C(d) 14.85 P-value 0.5354 2 1 C(d) 11.16 P-value 0.7993 3 6 C(d) 39.00 P-value 0.9591 4 4 C(d) 29.46 P-value 0.8898 5 1 C(d) 18.85 P-value 0.2763 6 1 C(d) 16.05 P-value 0.4498 7 1 C(d) 12.43 P-value 0.7140 8 2 C(d) 32.82 P-value 0.1079

10.69 0.8282 10.36 0.8470 39.68 0.9515 27.19 0.9388 14.99 0.5256 15.77 0.4691 21.52 0.1592 31.61 0.1369

2

3

4

5

6

26.76 0.9997 37.64 0.5770

31.52 0.9966 27.67 0.9301

28.17 38.02 42.64 0.9993 0.9686 0.9058 38.29 0.5475

34.56 0.9893 19.88 0.9968

30.78 46.83 29.87 0.9976 0.8038 0.9984 39.21 0.5056

19.84 0.7059

39.32 0.9557 40.27 0.4581

20.44 0.6714

Notes: The values of p are optimal chosen by the AIC.

W.-H. Huang, M.-C. Chao / Energy Policy 45 (2012) 730–738

737

Table 8 Estimation results of the linear model: 2000m6–2008m5. Model

Dependent variable

Error correction term coefficient

Wald test

DCPI1 1 2 3 4 5 6 7 8

DCPI DWPI DCPIFOOD DWPI DCPICLO DWPI DCPIHOU DWPI DCPITRA DWPI DCPIEDU DWPI DCPIMED DWPI DCPIMIS DWPI

nnn

 0.3272  0.0220  0.2585nnn  0.0086  0.8911  0.0074  0.4510nnn  0.0339  0.2090nnn  0.0157  0.3174nnn 0.0382  0.0517n  0.0030  0.1825nnn 0.0471

– 0.2596 – 0.0971 – 0.2693 – 0.1850 – 1.7602 – 1.4689 – 0.0055 – 0.0246

Q(4)

DWPI nnn

8.1881 – 2.3538n – 2.0717nn – 0.0166 – 0.6813 – 0.2125 – 1.5412 – 2.8390n –

DPOIL

DWEOIL

1.3140 25.9457nnn 1.8972 25.5503nnn 0.1087 24.2672nnn 0.2012 25.7941nnn 5.0566nnn 26.9910nnn 0.2863 26.7311nnn 0.7203 25.6731nnn 0.7422 23.7719nnn

0.3599 1.6506 0.4381 1.5484 1.4776 1.2934 2.3725n 1.6327 20.6058nnn 0.5314 0.4593 1.8634 0.6801 1.5476 0.4969 1.8100

6.93 0.83 8.79 0.63 3.74 0.69 0.36 0.53 1.25 0.95 6.84 0.84 0.56 0.50 3.76 0.64

(0.22) (0.93) (0.18) (0.96) (0.16) (0.95) (0.54) (0.97) (0.87) (0.92) (0.15) (0.93) (0.97) (0.97) (0.44) (0.96)

Adj R2

Lag

0.31 0.58 0.29 0.59 0.23 0.59 0.52 0.58 0.47 0.59 0.24 0.56 0.24 0.55 0.25 0.56

1 1 3 1 1 1 1 1 1 1 2 1 2 1 1 1

Notes: 1. The DCPI variable is replaced by DCPIFOOD, DCPICLO, DCPIHOU, DCPITRA, DCPIEDU, DCPIMED, and DCPIMIS in models 2, 3, 4, 5, 6, 7, and 8, respectively. 2. The value in the Wald test column is F statistics. The value in the parenthesis of the Q-test is p-value. 3. n, nn, and nnn represent 10%, 5%, and 1% significance levels, respectively.

exception of the DCPICLO and DCPIMED equations. The results of the Granger cause test indicate that DPOIL Granger causes DWPI in each model. However, the hypothesis that DPOIL does not Granger cause DCPIs can only be rejected in the DCPITRA equations. In the case of the short-run effects of DWEOIL, only the DCPITRA is Granger caused by DWEOIL. However, DWPI is not Granger caused by DWEOIL in any model. It is remarkable that the shortrun effects of DWEOIL on DWPI do not exist in the period of the leftwing party in power. This result is different from that of the full sample. The price transmission from domestic oil prices to the price indices is cut off in all except the CPI of transportation category, which is affected by changes in domestic oil prices. This result is primarily because of the left-wing government’s intervention in setting domestic oil prices. However, as previously mentioned, changes in international oil prices still have more critical effects than changes in domestic oil prices on the wholesale price indices. Therefore, the government’s intervention in setting oil prices to minimize volatility in the price indices is ineffective.

4. Conclusions In this paper, we employ the multivariate threshold model developed by Tsay (1998) to examine the effects of oil prices on the price indices in Taiwan using monthly data from January 1999 to March 2011. We use changes in international oil prices and changes in domestic oil prices as the threshold variables. The empirical results show that threshold-type nonlinearity exists in some classified CPI models, including the CPI, CPIFOOD, CPITRA, and CPIEDU models. Once the model rejects the linearity that the TAR model is employed to estimate, the linear autoregression model is adopted. We find that, in general, the adjustment speeds of the error correction mechanism are fast when the oil price changes are small and slow when the oil price changes are large. However, in the CPI of the transportation category model, the speed of the long-run adjustment in the regime with high oil price fluctuations is faster than that in the regime with moderate oil price changes. Hence, the consumer price index of the transportation category is sensitive to the oil price changes when the oil price shocks are great. The results of the Granger cause tests show that DWEOIL does not Granger cause DCPI and DWPI when the oil price change exceeds the

threshold level. This result may be because of the government’s intervention in the oil pricing policy. When the oil price shocks are greater, the government’s intervention in the price setting increases. Although the effects of domestic oil prices on the price indices are not significant when the oil price changes are large, international oil prices do have substantial effects on the price indices. International oil price shocks may affect the price indices through the effects of announcement and expectation. Thus, controlling domestic oil prices to prevent volatility in the price indices is ineffective. If we focus on the period when the left-wing party is in power, we find that DPOIL Granger causes DWPI in each model. The hypothesis that DPOIL does not Granger cause DCPIs can only be rejected in the DCPITRA equations, however. Moreover, it is remarkable that the short-run effects of DWEOIL on DWPI do not exit during the reign of the left-wing party. This result is different from that of the full sample. The price transmission from domestic oil prices to the price indices is cut off with the exception that the CPI of the transportation category is affected by changes in domestic oil prices. This result is due to the intervention of the left-wing government in setting domestic oil prices. We also find that changes in international oil prices still have more critical effects on the wholesale price indices. Therefore, the government’s intervention in setting oil prices to prevent volatility in the price indices is ineffective. In this study, we find that the international oil prices are more important than domestic oil prices to the price indices. The government’s intervention in domestic oil price is ineffective to the price stability, because the price indices still are still influenced by international oil prices. Moreover, freezing domestic oil price to stabilize the price level goes against the aim of energy saving. The more correct strategy is let the domestic oil prices return to market mechanisms, restrain the oil consumption, and induce the use of alternative energy. To prevent oil shocks, the government should put emphasis on building people’s confidence in the price stability, avoid people’s excessive expectations, and stop the price level increasing further more.

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