The effects of oil prices on inflation, interest rates and money

Energy 36 (2011) 4158e4164

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The effects of oil prices on inflation, interest rates and money Man-Hwa Wu a, *, Yen-Sen Ni b a b

Department of Finance, Ming Chuan University, 250 Zhong-Shan N. Rd., Sec. 5, Taipei 111, Taiwan, ROC Graduate Institute of Management Sciences, Tamkang University, Tamsui, Taipei Hsien 251, Taiwan, ROC

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 September 2010 Received in revised form 12 April 2011 Accepted 14 April 2011 Available online 24 May 2011

Recently, most of the relevant studies (see, e.g. Atukeren [5], Ayadi [6], Roeger [38], Trehan [44], Bermingham [7], Oladosu [34]) have focused on oil price shocks to the economy variables such as GDP, interest rates, inflation, and industrial production, but few studies have focused on external shocks to the possible reaction of monetary policies. Thus, this paper includes money variables in empirical models and investigates the relationships among oil prices, inflation, interest rates and money. The monetary policy might take time to be effective, so the concerns of lag-chosen issues will be vital issues from the aspect of this research. Then, different lag-chosen criteria and symmetric and asymmetric lag-lengths chosen are placed in a stressed situation in this study with regard to monetary lag concerns. We find that the empirical results are quite robust concerning various lag-chosen criteria, symmetric and asymmetric models, and different time series models. So, it implies that monetary policies still matter after accounting for the oil prices, the energetic variable, with the above robustness concerns. Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.

Keywords: Monetary policy Oil price Lag-chosen criteria

1. Introduction Issues about oil are important for the sustainment of human life. Recent studies have been published from researchers such as from Kerschner and Hubacek [28], Gallo et al. [13], and Alghalith [3]. Kerschner and Hubacek [28] who have studied the potential effects of world oil production to facilitate the development of adaptation policies. Their results imply that some industries including the financial services will be affected by oil production. Gallo et al. [13] propose that the growth in demand for oil in international markets is often identified as the main source of consumption pressure on prices. Alghalith’s [3] study shows that there is a connection between energy price and manufacturing price uncertainty. From the above findings, we realize that there is a close relationship between oil prices and real world activity. Therefore, it stimulates our interest to study the issues about oil prices, and whether the Fed’s policies could affect real activity caused by oil price shocks. Many academic economists are concerned about the relationship between oil prices and economic activity, and they find considerable consequences of oil price fluctuations on economic activity. Hamilton [20] find out that oil price shocks and real economic activity have been strongly correlated in the U. S. since World War II, even during the period from 1948e1972 prior to the first oil price shock. Later, * Corresponding author. Tel.: þ886 2 2882 4564x2390; fax: þ886 2 2880 9769. E-mail addresses: [email protected] (M.-H. Wu), [email protected] (Y.-S. Ni).

other researchers extend Hamilton’s basic findings using alternative data and estimation procedures (see, e.g. Burbridge and Harrison [9], Gisser and Goodwin [14]). Recently, Mork [33] and Hamilton [18,19] find that the effects of oil price changes on the economy are asymmetric. However, the related studies mostly focus on oil price shock to economy variables, such as GDP, interest rates, inflation, and industrial production. Few studies include monetary variables together with macroeconomic variables. However, the reaction of monetary variables to external shock is quite important from the perspective of government concerns. Therefore, we examine the relationships among oil prices, inflation, interest rates and money. In this paper, we investigate the relationships among oil prices, inflation, interest rates and money with greater robustness considerations such as different lag-length chosen criteria, choosing symmetric and asymmetric lag lengths, and applying different but appropriate models such as VAR and SUR models to test the above issue. The empirical results show the robustness results after considering the concerns mentioned above, namely, employing different lags suggested by several lag-chosen criteria and employing symmetric and asymmetric models won’t cause different results by applying different models such as VAR and SUR. Especially, after taking into account monetary lag concerns, we find that the empirical results are still quite robust by considering the different lag-chosen criteria, the symmetric and asymmetric models, and the different time series models. Therefore, it implies that monetary policies still matter after including the oil prices, the energetic variable, with the robustness concerns mentioned above.

0360-5442/$ e see front matter Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.04.028

M.-H. Wu, Y.-S. Ni / Energy 36 (2011) 4158e4164

The remainder of this paper is organized as follows. In Section 2, we review the related literature. Section 3 summarizes the hypothesis from the literature described in the section above. Section 4 details the data and the empirical results. A summary and the concluding remarks are given in the last section. 2. Literature review Although there are many papers related to macroeconomic variables with oil prices (see, e.g. Atukeren [5], Ayadi [6], Roeger [38], Trehan [44], Bermingham [7], Oladosu [34]), few papers have investigated macroeconomic and monetary variables together with oil prices. Thus, we not only examine the behavior of macroeconomic variables but also the monetary variables’ reactions to oil prices. Moreover, different lag-length chosen by the criteria (see, e.g. Akaike [1], Rissanen [37], Schwarz [40], Shibata [41], Hacker and Hatemi-J [16]) are concerns raised by this paper, which are seldom mentioned in relevant research studies. In this section, we discuss four parts of related literature. Part 1 mentions the relationship between oil price and inflation, part 2 describes the relationship between oil price and interest rates, part 3 discusses the relationship between oil price and money, and the last section introduces lag-length chosen criteria employed in this paper. 2.1. Literature review on the relationships between oil prices and inflation Hooker [21] provides formal evidence of the change in the relationship between oil prices and inflation for the period 1962e2000. Statistical tests find a break at the end of 1980. Thus, he investigates two sub-periods, namely, the period from 1962 to 80 and the period from 1981 to 2000, and finds that oil prices have a significant impact on inflation in the earlier period, but not in the later period. In addition, Trehan [44] focuses on the relationship between oil prices and inflation, and he also finds that oil prices have a significant impact on inflation in the earlier sample period. Additionally, Roeger [38] analyzes the short-term and long-term quantitative impact for a permanent oil price increase for output and inflation in the European region. The results show that there is no severe inflation risk, but there is a short-run tradeoff between inflation and output, i.e. the oil prices at least play an important role in the short run for the European region. Furthermore, Cunado and Perez [12] study the impact of oil price shocks on both economic activity and consumer price indices for six Asian countries. The evidence suggests that oil prices have significant effects on both economic activity and price indices. Recently, Bermingham [7] re-examines the influence of oil prices on inflation in a small open economy- Ireland, and his study provides the impact of the oil price increases on inflation by quantifying approaches. Then, Jacquinot et al. [24] explore the links between oil prices and inflation in the euro area, and focus on the impact on inflation in the short and medium run. The results show that changes in oil prices are of vital importance for the understanding of inflation in the short run, but that their impact on inflation is much more complex and depends on the initial shock for the long run. Castillo et al. [11] also analyze the relationship between average inflation and oil price volatility, and they find that higher oil volatility induces higher levels of average inflation. Their model shows that average inflation will rise while increasing the volatility of the oil prices. Different from the relevant studies, we employ the symmetric and asymmetric models to reexamine the effect of the oil prices on inflation by applying several lag-chosen criteria with more robustness concerns in this study.

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2.2. Literature review on the relationships between oil prices and interest rates Sadorsky [39] shows that oil price movements explain a larger fraction of the forecast error variance in real stock returns, as compared with the interest rate, and oil price volatility shocks have asymmetric effects on the economy as well. Then, Papapetrou [36] attempts to shed light on the relationships among oil prices, real stock prices, interest rates, real economic activity and employment for Greece. The empirical evidence indicates that the oil price indeed affects real economic activity and employment. Lowinger et al. [30] examine the relationship between the interest rate in world financial markets and the oil prices, but their results show that only very large oil prices increase will have a significant impact on the world interest rate, i.e. oil prices show a non-negligible sensitivity to the world interest rates. In addition, Steidtmann [42] finds that the rise in oil prices means that interest rates are headed higher throughout the 1970s. It indicates that higher oil prices will cause inflation, translates into higher interest rates, and sends the economy into a recession. Recently, Manera and Cologni [31] study the direct effects of oil price shocks on macroeconomic variables such as output and prices by a structural cointegrated VAR model. The empirical results show that there seems to be an impact of unexpected oil price shocks on interest rates, suggesting that a contractionary monetary policy response is directed to fight inflation. Oil prices will be able to affect the interest rate. Thus, the above issue might be a worthwhile topic to examine the relationship between oil prices and inflation by employing symmetric and asymmetric time series models. In addition, we also apply different lag-chosen criteria to test if the above phenomena exist for robustness concerns of this empirical study. 2.3. Literature review on the relationships between oil prices and money In related literature, Hoover and Perez [22] indicate that the US recessions over the past 30 years have been preceded by oil price increases and by contractionary monetary policies. Moreover, Bernanke et al. [8] also reveal that most of the impact of oil price shocks on the real economy is attributable to the Fed’s tightening in response to adverse oil price shocks. They find that most of the reduction in US outputs is accounted for by tightening monetary policy in response to adverse oil price shocks. Then, Lee et al. [29] study monetary policy, oil price shocks for Japan, and find that oil price movement have a significant forecasting power on Japanese monetary policy. Thus, increases in oil prices induce increases in the interest rate, and then strengthening the contractionary monetary policy in response to the oil shocks. Recently, Jumah and Pastuszyn [26] examine the relationship between the oil price and aggregate demand in a developing country, Ghana, via the interest rate channel by time series analysis. Their results indicate that monetary policy is initially eased in response to a surge in the price of oil in order to lessen the pressure of economic growth, but at the cost of higher inflation. Miguel et al. [32] address the question of why the effects of oil shocks from the mid-1980s on output and inflation were smaller, and their results support the hypothesis of smaller macroeconomic effects of oil shocks from the mid-1980s. The results show that both the labor market rigidities and the oil shares have decreased over time and a contractionary monetary policy are employed in order to control inflation. Furthermore, Castillo et al. [11] not only show that oil price volatility can generalize sizeable levels of average inflation, but also suggest that a more conservative monetary policy could mitigate

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those effects. Oppositely, Askari and Krichene [4] indicate that monetary policy variables, such as interest rates and exchange rates, have a powerful effect on oil markets. Their results show that monetary policy affects oil markets through the channel of the interest rates and the exchange rates, and the interest rates have significant impacts on oil demand. Moreover, Carlstrom and Fuerst [10] discuss the efficiency of different U.S. federal fund rate movements in response to oil price increases, and show that increasing oil prices will raise the inflation rate. In addition, Manera and Cologni [31] study the direct effects of oil price shocks on output and prices by a structural cointegrated VAR model and the empirical results show that there seems to be an impact of unexpected oil price shocks on interest rates, suggesting a contractionary monetary policy response directed to fight inflation. Therefore, the relationships among oil price changes, inflation, interest rates and money will be worthwhile to be retested by more robustness concerns as mentioned above. 2.4. Literature review of the lag-length chosen criteria Message transmission is an essential concern for financial models, so the lag-length chosen will be emphasized in this paper. In addition, the lag-length chosen means how many lags are necessary to be included in time series models. Thus, we include the viewpoints of several scholars in the literature reviews mentioned below. Hsiao [23] proposes the FPE criterion, which adopts a stepwise procedure based on Granger’s concept of causality. This criterion is suggested as a practical means to identify the order of lags for each variable in a multivariate autoregressive model, since the model is able to serve as a reduced form formulation to avoid imposing spurious restrictions on the model. Similarly, Thornton and Batten [43] suggest that the FPE criterion performs well in selecting a lag-length for a model based on a standard, classical and hypothesis-testing. However, the FPE criterion may not conform to all researchers’ prior beliefs about the appropriate tradeoff between bias and efficiency. Different from Thornton and Batten [43]’s results, Jones [25] shows that a particular method for lag-length determination reveals a better performance than the statistical methods in correctly assessing the causal relationship between money growth and inflation. Moreover, the lag terms selected by the FPE criterion seems to be inadequate for testing Granger causality as proposed by Kang [27]. Kang’s research reveals that the lag terms should have the most efficient forecasting power in order to test the causality between the industrial production and the leading indicators by his new approach as suggested that the ARIMA model is better than the pure AR model when employing the FPE criterion. Additionally, Ozcicek, and McMillin [35] apply the Monte Carlo simulation method to evaluate the lag-length selection, and their results imply that the AIC criterion dominates for the symmetric lag models, and the AIC criterion is more frequently used for selecting the lag-length of a model than for other criteria. As well documented in the above literature, the traditional information criterion such as the Bayesian Information Criterion (BIC) (Rissanen [37]) is employed if the autocorrelation of the process is especially characterized by large negative components. In addition to the popular AIC and BIC, we also evaluate the Shibata Criterion (SC) (Shibata [41]). The SC criterion converges to the BIC criterion as selecting lower orders while the sample size increases especially for moderate MA parameters. As for the AR coefficients, the SC selects a higher order for small samples, but converges to BIC as the sample size increases. Thus, both criteria are included in this paper for robustness concerns. Furthermore, Hatemi-J [17] combines two lag-chosen criteria to form a new HJC criterion (HJC). He applies a Monte Carlo simulation experiment to find the

performance for choosing the optimal lag order in VAR models by employing the HJC criterion, and obtains that the empirical results are similar to the results chosen by the AIC criterion. From the above literature, the lag-length chosen concerned by many scholars seems to be a critical issue in empirical studies. We are afraid that different lag-chosen criteria might cause dissimilar results. Thus, we investigate whether empirical results would be unlike by using different lag-length chosen criteria in testing the relationships among oil price changes, inflation, interest rates and money, namely, we would like to find whether the empirical results are sensitive to different lag-length chosen criteria, such as the AIC,1 BIC,2 FPE,3 SBC,4 SC,5 and HJC6 criteria. In addition, we examine whether the empirical results employed by the asymmetric lag length are different from those employed by symmetric lag lengths while concerning the above lag-length chosen criteria. Thus, our study should be a worthwhile topic to investigate the relationships among oil prices, inflation, and interest rates concerning the symmetric and asymmetric models by employing six lag-chosen criteria with more robustness concerns, which will be crucial for empirical studies.

3. The hypotheses According to the literature reviews discussed above, these following hypotheses are derived as follows Hypothesis 1: Oil prices will Granger cause inflation. Studies from Trehan [44] and Cunado and Perez [12] do not investigate how the empirical results are different from each other as concerning different lag-length chosen criteria in the empirical study. Hypothesis 2: Oil prices will Granger cause interest rates. Studies from Papapetrou [36] and Steidtmann [42] do not investigate how the empirical results are different from each other as concerning different lag-length chosen criteria. Hypothesis 3: Oil prices will Granger-cause money. Studies from Bernanke et al. [8] and Lee et al. [29] do not investigate how the empirical results are different from each other as concerning different lag-length chosen criteria. Hypothesis 4: Oil prices will Granger cause inflation, interest rates and money.

1 AIC (Akaike’s Information Criterion) criterion, proposed by Akaike in 1973 and P P 1974 [2]. AIC ¼ Tlogj j þ 2N Here, T is the number of usable observations, j j is the determinant of the variance/covariance matrix of the residuals, and N is the total number of parameters estimated in all equations. 2 BIC (Bayesian Information Criterion) criterion, proposed by Rissanen in 1978. P P BIC ¼ logj j þ NlogðTÞ=T Here, T is the number of usable observations, j j is the determinant of the variance/covariance matrix of the residuals, and N is the total number of parameters estimated in all equations. 3 FPE (Final Prediction Error) criterion, proposed by Akaike in 1969 and 1970. FPE ¼ ðT þ n þ 1Þ=ðT  n  1ÞSSRðnÞ=T Here, T is the sample size, n is the lag-length being tested, SSR is the sum of the squared residuals, and N denotes the maximum lag-length over which the search is carried out. 4 SBC (Schwarz’s Bayesian Criterion), proposed by Schwarz in 1978. P P SBC ¼ Tlogj j þ NlogðTÞ Here, T is the number of usable observations, j j is the determinant of the variance/covariance matrix of the residuals, and N is the total number of parameters estimated in all equations. P 5 SC (Shibata Criterion), proposed by Shibata in 1980. S ¼ T logj jþ T logðT þ 2NÞ P Here, T is the number of usable observations, j j is the determinant of the variance/ covariance matrix of the residuals, and N is the total number of parameters estimated in all equations. 6 ^ Þþ HJC criterion, is proposed by Hacker and Hatemi-J in 2001. HJC ¼ lnðdetU j ^ is the jðn2 lnT þ 2n2 lnðlnTÞ=2TÞ; j ¼ 0; 1; 2; .; k Here, T is the sample size, U j maximum likelihood estimate of the variance-covariance matrix U when the lag order used in the estimation is j, and n is the number of variables.

M.-H. Wu, Y.-S. Ni / Energy 36 (2011) 4158e4164 Table 1 Statistics for the Oil, IR, CPI and M2. Variable

Oil (cents per barrel) IR (percent per annum) CPI (index 1990) M2 (billions of U.S. dollars)

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Table 2 Unit root tests for Oil, IR, CPI and M2.

1995.1e2005.12 Mean

Standard deviation

Minimum

Maximum

23.26 3.78 100.13 4946.84

10.31 1.74 7.73 1013.44

8.18 0.89 87.28 3500.20

57.87 6.18 115.68 6674.10

Studies from Carlstrom and Fuerst [10] and Manera and Cologni [31] do not investigate how the empirical results are different from each other as concerning different lag-length chosen criteria.

Level

Variable

Trend

ADF (t)

DF (t)

APP (t)

PP (t)

Oil

No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes

1.07 1.94 1.31 0.65 0.10 3.49* 1.45 2.52 7.57* 7.63* 4.65* 4.77* 9.73* 9.71* 6.34* 6.34*

0.30

0.01 1.51 1.19 0.03 0.33 2.54 1.98 2.85 8.18* 8.21* 6.26* 6.40* 8.12* 8.09* 8.82* 8.79*

0.30

IR CPI M2 Log differencing

Oil IR CPI

Hypothesis 5: The empirical results will be different while employing the different lag-chosen criteria to test the relationship between oil prices and other variables. Because few studies are concerned about employing different lag-length chosen criteria for time series models for robustness concerns, we would apply different lag-chosen criteria to test whether the empirical results are different. Hypothesis 6: The empirical results will be different while employing the symmetric models and symmetric models7 as different lag-chosen criteria to test whether the empirical results are different. Because few studies focus on the robustness of empirical results by choosing symmetric models and asymmetric models, we would apply the symmetric models and asymmetric models to test whether the empirical results are different.

4. Empirical results We obtain the U.S. monthly data of FOB costs of crude oil imports, the Treasury bill rate, the consumer price index, and the M2 from the period from January 1995 to December 2005 in the AREMOS database established by the Taiwan Economic Data Center and Economagic database, respectively. The FOB costs of crude oil imports, Treasury bill rate, consumer price index, and M2 are regarded as oil price variables, interest rate variables, inflation variables and monetary variables respectively in this paper. 4.1. Data and unit root tests Table 1 includes the means, standard deviations, minimums, and maximums for the oil prices (Oil), the interest rates (IR), the consumer price index (CPI) and the money (M2). In Table 1, the means for the Oil, IR, CPI and M2 are 23.26, 3.78, 100.13 and 4946.84, and the standard deviations for the Oil, IR, CPI and M2 are 10.31, 1.74, 7.73, and 1013.44, respectively. Stationary tests are employed in order to prevent spurious results before the time series models are set up. Thus, unit root tests are applied to test the stationary of the Oil, IR, CPI and M2 series. In Table 2, we find that the DickeyeFuller (DF), Augmented DickeyeFuller (ADF), PhillipsePerron (PP) and Augmented PhillipsePerron (APP) values of the levels of Oil, IR, CPI and M2 are all insignificant at the 5% level, i.e. all of the series are not regarded as stationary series. After log-differencing these four series, the DF, ADF, PP and APP values for Oil, IR, CPI and M2 are significant, i.e. these logdifferential series are stationary, as shown in Table 2. Additionally, the meaning of these four log-differential variables for OIL, IR, CPI and M2

7 One model chooses the same lag-length for different variables, and the other model chooses different lag lengths for different variables [15].

M2

1.17 0.45 2.24 *

8.11

6.40* 7.93* 8.86*

1.17 0.45 2.24 8.11* 6.40* 7.93* 8.86*

DF (t) indicates the value of the DickeyeFuller test, ADF (t) depicts the value of the Augment DickeyeFuller test, PP (t) indicates the value of the PhillipsePerron test, and APP (t) represents the value of the Augmented PhillipsePerron test. The lag-length of ADF and that of APP are chosen by the AIC criterion. Star (*) indicates significance at the 5% significance level.

Table 3 The abbreviated symbols of variables and criteria. Oil IR CPI M2 GOIL GIR INF GM2

Oil price Treasury bill rate Consumer price index Money Oil price changes Interest rate changes Inflation (consumer price changes) Money growth changes

AIC BIC FPE HJC SBC SC

Akaike’s Information Criterion Bayesian Information Criterion Final Prediction Error Hacker and Hatemi-J’s Criterion Schwarz’s Bayesian Criterion Shibata Criterion

are defined as oil price changes (GOIL), interest rate changes (GIR), inflation (INF), and money growth (GM2)8 respectively. 4.2. Granger causality results This section includes three sub-topics, to examine the relationship between the GOIL and the GIR, to investigate the relationship between the GOIL and the INF, and to explore the relationship between the GOIL and the GM2. Moreover, the above relationships are examined by both symmetric models and asymmetric models with concern for the lag-length chosen criteria such as the AIC, SBC, BIC, SC, HJC, and FPE criterion. Since presenting empirical results are quite complicated, we set up a table containing the above information for readers to more easily understand what we have done in this paper. Furthermore, we set all of the information in a single table instead of listing them in several tables in order to save space. In addition, since several variables and lag-chosen criteria are mentioned frequently, we then use the abbreviated symbols instead of presenting the full names as shown in Table 3. 4.2.1. Examining the relationship between the GOIL and macroeconomic variables by applying symmetric models In the 2 by 2 systematic VAR models, the Granger causality results for three VAR models are investigated, such as the Granger causality tests for GOIL and GIR, the Granger causality tests for GOIL and INF, and the Granger causality tests for GOIL and GM2. Furthermore, all of

8

Money growth could be regarded as a monetary policy variable.

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Table 4 Granger causality tests. GOIL and GIR Symmetric models AIC

GOIL andINF Asymmetric models (1) (1, 3) H1 : 1.43 (2) (2, 5) H2 : 2.16 (1) (1, 3) H1 : 1.43 (2)(1, 1) H2 : 1.12 (1) (14, 14) H1 : 1.32 (2) (15, 15) H2 : 1.70 (1) (1, 1) H1 : 1.73 (2) (1, 1) H2 : 1.12 (1) (15, 7) H1 : 1.20 (2)(15, 5) H2 : 1.61 (1) (1, 1) H1 : 1.73 (2) (1, 1) H2 : 1.12

(1) (1, 1) H1: 3.44 (2) (1, 1) H2:0.79

BIC

FPE

SBC

(1) (1, 1) H1: 3.44 (2) (1, 1) H2:0.79

SC

HJC

GOIL andGM2

Symmetric models

Asymmetric models

Symmetric models

Asymmetric models

(1) (1, 1) H1: 34.73* (2) (1, 1) H2:13.72*

(1) (1, 2) H1 : 37.20* (2) (1,1) H2:7.66* (1) (1, 2) H1 : 37.20* (2) (1, 1) H2:7.66* (1) (1, 8) H1 : 22.75* (2) (15,14) H2:1.87* (1) (1, 2) H1 : 37.20* (2) (1, 1) H2: 7.66* (1) (14,12) H1 : 4.75* (2) (15, 9) H2: 2.52* (1) (1, 2) H1 : 37.20* (2) (1, 1) H2 : 7.66*

(1) (1, 1) H1: 1.15 (2) (1, 1) H2:2.12

(1) (1, 1) H1 : 28.86* (2) (2, 1) H2 : 2.34 (1) (1, 1) H1 : 28.86* (2) (1,1) H2 : 1.79 (1) (1, 1) H1 : 28.86* (2) (15, 15) H2 : 1.24 (1)(1, 1) H1 : 28.86* (2) (1, 1) H2 : 1.79 (1)(9, 5) H1 : 3.76* (2) (15, 13) H2 : 1.41 (1) (1, 1) H1 : 28.86* (2) (1, 1) H2 : 1.79

(1) (1, 1) H1: 34.73* (2) (1, 1) H2:13.72*

(1) (1) (1, 1) H1: 1.15 (2) (1, 1) H2:2.12

Equation (1) means “Oil price changes do not Granger- cause the growth of the interest rate” in the case of GOIL and GIR; “Oil price changes do not Granger- cause Inflation” in the case of GOIL and INF; “Oil price changes do not Granger- cause money growth” in the case of GOIL and GM2. Equation (2) means “The growth of interest rate does not Granger-cause oil price changes” in the case of GOIL and GIR; “Inflation does not Granger-cause oil price changes” in the case of GOIL and INF; “Money growth does not Granger-cause oil price changes” in the case of GOIL and GM2. (a, b) in the table, the represented lag lengths a and b are chosen for the money variable and inflation variable for Equation (1) and Equation (2). Star (*) indicates significance at the 1% level. The criteria for BIC, SC, HJC and FPE are selected equation by equation. This is valid since symmetric lag models will select the same lag length for each variable in each equation. Therefore, the spaces are empty due to this reason. The above Granger causality tests for symmetric and asymmetric models employ six different lag-chosen criteria.

Table 5 The estimated VAR model9 of GOIL, GIR, INF and GM2. Dep. Var.

GOIL GIR INF GM2

Indep. Var. GOIL(1)

GIR(1)

INF(1)

GM2(1)

21.91** 0.65 34.35** 0.21

0.68 32.33** 0.57 4.57*

12.99** 4.30* 3.24 1.03

0.64 3.94* 0.95 4.69*

Star (*) indicates significance at the 5% level, and two stars (**) means significance at the 1% level. The numbers in the tables are the F-Statistic of the Granger causality test. The numbers in parentheses are the lag-length of the Granger causality test.

these Granger causality results are tested by employing different lag-chosen criteria for robustness. In the first and third Granger causality tests, the results selected by the AIC and the SBC criteria don’t show significant effects. However, the second Granger causality results show the feedback effects between the GOIL and the INF, implying the GOIL Grangercause the INF, and the INF Granger-cause the GOIL. Thus, it means that the oil prices, the important energetic prices, play an important role in causing the inflation.

these OLS equations. In Table 4, we find that the Granger causality results are insignificant by applying different lag-chosen criteria in the first OLS equation. In addition, the feedback effects between the GOIL and the INF and an unidirectional effect from the GOIL to the GM2 are shown in the Table 4 (Table 5). 4.2.3. Examining the relationships among the GOIL, the GIR, the INF and the GM2 By employing 4 by 4 systematic VAR models, some interesting phenomena are revealed. For example, the results show feedback effects between GOIL and INF, feedback effects between GM2 and GIR, and unidirectional effects from INF to GIR. The results imply that the GOIL have significant effects on the INF, the GM2 have effects on the GIR, and the INF have effects on the GIR. In addition, some of the above findings coincide with the empirical results shown in Section 4.2.1 and 4.2.2. The empirical findings results are similar to the studies of Trehan [44] and Papapetrou [36] as mentioned in the literature reviews.

Table 6 Regression results. Dep. Var.

4.2.2. Examining the relationship between the GOIL and macroeconomic variables by applying asymmetric models We apply asymmetric models by separating 2 by 2 VAR models into two OLS equations. We also use the six lag-chosen criteria for

9

The VAR model chooses lag 1 for an estimated lag-length by the AIC and SBC criteria.

Indep. Var. GOIL

INF GIR GM2

0.0123* 0.0447 0.0070

GIR 0.0040 e 0.0153*

INF

GM2

e 1.8967 0.1576

0.1230 5.7315* e

The numbers in the table represent the coefficients of estimated equations. * indicates a 5% significance level. The dependent variables are INF, GIR, and GM2, and the independent variables are the GOIL and the macroeconomic variables.

M.-H. Wu, Y.-S. Ni / Energy 36 (2011) 4158e4164 Table 7 The empirical results of the symmetric models. Panel A Indep. Var. GOIL(1) 0.0164* GOIL(1) 0.1064 GOIL(1) 0.0036

Dep. Var. INF GIR GM2

INF(1) 0.1601 GIR(1) 0.4950* GM2(1) 0.2365*

Panel B Dep. Var. INF GIR GM2

Indep. Var. GOIL(7) 0.0095* GOIL(1) 0.0488 GOIL(3) 0.0034

GIR(7) 0.0080 GIR(1) 0.4428* GIR(3) 0.0067

INF(7) 0.1094 INF(1) 3.6128* INF(3) 0.0547

GM2(7) 0.1223 GM2(1) 2.9923 GM2(3) 0.0965

The numbers in the table represent the coefficients of estimated equations. The numbers in the parentheses represent the lag-chosen for the variable. * represents a 5% significance level.

4.3. Analysis for the contemporary factors affecting the INF, the GIR, and the GM2 Table 6 reveals the factors affecting the INF, GIR, and GM2. By taking the contemporaneous effect into account for the INF, the GIR, and the GM2, the results show that the GOIL have effects on INF, the GM2 have effects on GIR, and the GIR have effects on the GM2. In other words, one macroeconomic variable might have contemporaneous effect to another macroeconomic variable, namely the GOIL affect the inflation, The GM2 affect the GIR, and the GIR affect the GM2 (Table 7). 4.3.1. Analysis of lag factors for the GOIL to the INF, the GIR, and the GM2 in the symmetric lag models In the systematic models, we employ both a 2 by 2 VAR model and a 4 by 4 VAR model to find the lag effect instead of the contemporaneous effect for the INF, the GIR, and GM2. In the 2 by 2 VAR model, we find that the GOIL have effects on INF by employing in the 2 by 2 VAR model. Furthermore, we also find that both the INF have effects on the GIR and the GOIL have effects on the INF in the 4 by 4 VAR model. In addition, we also find that the GOIL have unidirectional effects on the INF which is robust with the above findings (Table 8). Table 8 The empirical results of the asymmetric models. Panel A Dep. Var. INF

Indep. Var. GOIL(1) 0.0171* GOIL(1) 0.1254* GOIL(1) 0.0036

GIR GM2

INF(2) 0.2030* GIR(3) 0.4082* GM2(1) 0.2365*

Panel B Dep. Var. INF GIR GM2

Indep. Var. GOIL(1) 0.0169* GOIL(1) 0.0606 GOIL(1) 0.0016

GIR(1) 0.0062 GIR(3) 0.3419* GIR(1) 0.0098*

INF(2) 0.2419* INF(1) 3.5469 INF(1) 0.1043

GM2(2) 0.1057 GM2(1) 4.3348* GM2(1) 0.1923*

The numbers in the table represent the coefficients of estimated equations. The numbers in the parentheses represent the lag-chosen for the variables selected by the AIC criterion. * indicates a 5% significance level.

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4.3.2. Analysis of the lag factors for the GOIL to the INF, the GIR, and the GM2 in the asymmetric lag models In the asymmetric models, we employ both 2 by 2 VAR models and 4 by 4 VAR models by selecting different lag-lengths according to the AIC criterion. In the 2 by 2 VAR models, we find that the GOIL Granger-causes both INF and GIR, revealing that the GOIL will affect the INF and the GIR at the 5% significance level. In the 4 by 4 VAR models, the evidence shows that the GOIL affect the INF. With more robustness concerns recommended by this study, the empirical evidence shows that the GOIL will affect the INF by both symmetric models and asymmetric models. 5. Conclusions The paper uses the U.S. monthly data of FOB costs of crude oil imports, the Treasury bill rate, the consumer price index, and the M2 from the period from January 1995 to December 2005 in the AREMOS database established by the Taiwan Economic Data Center and the Economagic database, respectively. We mainly test if the energetic variable, the oil prices, will have contemporaneous or lag effects on interest rates, consumer prices, and money by employing time series models, and derive several important findings resulting from the above hypotheses in this paper. Firstly, the empirical results reveal that the GOIL will affect the INF, and the INF will affect the GOIL as shown in both symmetric and asymmetric models. Namely, the feedback effects exist between GOIL and INF. In addition, the GM2 and the GIR have a feedback effect and unidirectional effects from INF to GIR are shown in the VAR models as well. Secondly, the oil prices, the important energetic prices, affect inflation by employing the symmetric and asymmetric lag models and the lag and contemporaneous effects from the oil prices to inflation are also derived. There are six different lag-length criteria suggested by many scholars as mentioned in the literature review. Additionally, the findings are quite crucial in this study, since no matter what concerns are mentioned above, the GOIL will have a contemporaneous and time lag effect on the INF. Thirdly, the transmission from the GOIL to the INF could be decided by the laglength chosen criteria. In the literature review, many scholars propose numerous lag-length criteria. In addition, the empirical results might be quite sensitive while employing different laglengths, namely the empirical results might be different while employing different lag-lengths in the time series models. Finally, the empirical results, the GOIL will cause inflation, and are almost the same as choosing either symmetric or asymmetric models which concern several lag-length criteria. Furthermore, the above lag-length concerns are quite crucial but neglected by a great deal of empirical research, since it is seldom mentioned in the empirical research. Moreover, if time series models are tested with more lag-length criteria, the empirical results will be more reliable and trustworthy with more robustness concerns. Otherwise, the empirical results might not be suspicious if the results are employed by a special laglength criterion. In summary, we conclude that the GOIL will affect and Grangercause the INF, which coincide with the results employing either the symmetric models or the asymmetric models. In addition, and the findings with more robust concerns are also coincident with the relevant literature (see, e.g. Trehan [44] and Lee et al.’s [29]). Additionally, the main contribution of this study is unique from others in that we have involved more concerns about various lag-length chosen criteria for different models including diverse time series models employed in this study. In fact, different models and singular lag-chosen criteria might be appropriate and objective to test empirical research studies. However, if the different models and different lag-chosen criteria yield dissimilar results, the empirical results might be suspicious. Thus, the robustness concerns for the

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