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

ARTICLE IN PRESS Resources Policy 34 (2009) 121–132

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Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy Shuddhasawtta Rafiq, Ruhul Salim , Harry Bloch School of Economics & Finance, Curtin University of Technology, Perth, WA 6845, Australia

a r t i c l e in fo

abstract

Article history: Received 15 April 2008 Received in revised form 4 September 2008 Accepted 11 September 2008

This paper empirically examines the impact of oil price volatility on key macroeconomic indicators of Thailand. Following Andersen et al. [2004. Analytical evaluation of volatility forecasts. International Economic Review 45(4), 1079–1110], quarterly oil price volatility is measured by using the realized volatility (RV). The impact of the oil price volatility is investigated using the vector auto-regression (VAR) system. The Granger causality test, impulse response functions, and variance decomposition show that oil price volatility has significant impact on macroeconomic indicators, such as unemployment and investment, over the period from 1993Q1 to 2006Q4. Perron’s [1997. Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics 80(2), 355–385] test identifies structural breaks in all the concerned variables during the time of the Asian Financial Crisis (1997–1998). A VAR for the post-crisis period shows that the impact of oil price volatility is transmitted to budget deficit. The floating exchange rate regime introduced after the crisis may be the key contributor to this new channel of impact. Crown Copyright & 2008 Published by Elsevier Ltd. All rights reserved.

JEL Classificaton: C32 Q43 O13 Keywords: Oil price volatility Thailand Granger causality test Impulse response function Variance decomposition

Introduction Oil, like other primary commodities, is a vital input in the production process of an economy. Primary commodity prices affect aggregate price levels positively as commodities are used as raw materials in industrial production (Bloch et al., 2006). Similarly, oil is needed to generate electricity, run production machinery, and transport the output to the market. Further, volatility in oil prices may reduce aggregate output temporarily as it delays business investment by raising uncertainty or by inducing expensive sectoral resource reallocation (Guo and Kliesen, 2005). Although industrialized developed countries seem to be more dependent on oil, evidence shows that the demand for oil in developing countries is on an increasing trend (Birol, 2007). Most of the earlier studies concerning oil price shocks or volatility and economic activities have been conducted in the context of developed economies; for example Hamilton (1983), Burbridge and Harrison (1984), Gisser and Goodwin (1986), Mory (1993), Mork and Olsen (1994), and Ferderer (1996), among others. Research concerning the impact of oil price volatility in the

 Corresponding author. Tel.: +618 9266 4577.

E-mail address: [email protected] (R. Salim).

context of developing countries is very limited. This is partly due to the lack of reliable data and partly due to the less historical dependence of these countries on oil. However, since these countries are presently experiencing increased demand for energy, a through investigation of the impact of oil price variability on these economies is warranted. This paper aims to analyze the impact of oil price volatility on different macroeconomic variables of Thailand, such as output, price level, unemployment, interest rate, fiscal deficit, investment, and trade balance. Thailand serves as a suitable case for four main reasons. Firstly, the Thai economy has a convincing track record of growth through privatization and widespread trade liberalization compared with other developing economies. Secondly, since Thailand has limited domestic oil production and reserves, and imports make up a significant portion of the country’s oil consumption; the Thai economy seems to be relatively more vulnerable to oil price changes compared with other developing countries with greater oil supply security. Thirdly, there exists no prior study of this type for Thailand. Finally, the accuracy and availability of the relevant data play an important role in choosing Thailand for study. The remainder of the paper is organized as follows. Macroeconomic implications of oil price volatility offers two different channels through which oil price volatility may impact the

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macroeconomy. Oil price and the economy presents a critical review of earlier literature followed by an analytical framework in Data sources and analytical framework. Empirical results from the estimation are presented in Analysis of findings. Conclusion and policy implications are offered in the final section.

Macroeconomic implications of oil price volatility It is now well established in both empirical and theoretical literature that oil price shocks exert adverse impacts on different macroeconomic indicators through raising production and operational costs. Alternatively, large oil price changes—either increases or decreases, i. e. volatility—may affect the economy adversely because they delay business investment by raising uncertainty or by inducing costly sectoral resource reallocation. Bernanke (1983) offers theoretical explanation of the uncertainty channel by demonstrating that, when the firms experience increased uncertainty about the future price of oil then it is optimal for them to postpone irreversible investment expenditures. When a firm is confronted with a choice of whether to add energy-efficient or energy-inefficient capital, increased uncertainty born by oil price volatility raises the option value associated with waiting to invest. As the firm waits for more updated information, it forgoes returns obtained by making an early commitment, but the chances of making the right investment decision increase. Thus, as the level of oil price volatility increases, the option value rises and the incentive to investment declines (Ferderer, 1996). The downward trend in investment incentives ultimately transmits to different sectors of the economy. Hamilton (1988) discusses the sectoral resource allocation channel. In this study by constructing a multi-sector model, the author demonstrates that relative price shocks can lead to a reduction in aggregate employment by inducing workers of the adversely affected sectors to remain unemployed while waiting for the conditions to improve in their own sector rather than moving to other positively affected sectors. Lilien (1982) extends Hamilton’s work further by showing that aggregate unemployment rises when relative price shocks becomes more variable.

Oil price and the economy Oil price changes impact real economic activities on both the supply and demand side (Jimenez-Rodriguez and Sanchez, 2005). The increase in oil price is reflected in a higher production cost that exerts adverse effects on supply. The higher production cost lowers the rate of return on investment, which affects investment demand negatively. Besides, increased volatility in oil price may affect investment by increasing uncertainty about future price movements. Consumption demand is also influenced by the changes in oil price as it affects product price by changing production cost. Moreover, a rise in oil prices deteriorates the terms of trade for oil-importing countries (Dohner, 1981). As oil is directly linked to the production process, it can have a significant impact on inflation, employment, and output. An oil price shock can increase inflation by increasing the cost of production. It also affects employment, as inflationary pressure may lead to a fall in demand and this, in turn, leads to a cut in production, which can create unemployment (Loungani, 1986). The employment–oil price relationship holds true for not only industrial production, it is equally true for agricultural employment (Uri, 1995). Previous research in this field mainly investigates two different aspects of the relationship between oil price and economic activities: the impact of oil price shock and the impact of oil price volatility. These two approaches differ in the way they

incorporate oil price in their model. While the first approach takes oil prices at their levels, the second approach employs different volatility measures to capture the oil price uncertainty. In response to two consecutive oil shocks in the early and late 1970s, a considerable number of studies have examined the impact of shocks to oil price levels on economic activities. Pioneering work by Hamilton in the early 1980s on the relationship between oil price and economic activities spurred researchers to look into the issue in greater detail. Hamilton (1983) analyzes the behavior of oil price and the output of the US economy over the period 1948–1981, and concludes that every US recession between the end of World War II and 1973 (except the 1960–1961 recession) has been preceded, with a lag of around three-fourths of a year, by a dramatic increase in the price of crude petroleum. He further notes that post-1972 recessions in the US were mainly caused by OPEC’s supply-oriented approach. In his subsequent works, Hamilton (1988, 1996) strengthens his conviction that there is an important correlation between oil shocks and recession. In a recent survey, Hamilton (2008) further stresses the importance of oil price on macroeconomic activities. Since then a number of researchers have supported and extended Hamilton’s results. Mork (1989) examines the relation between oil price change and GNP growth in the US with an extended data set (1948–1988) to capture the effect of both upward and downward movements of oil price on output. Hamilton considers only large upward price movements and finds that there is a significant negative correlation between oil price and output. The major contribution of Mork’s study is that it finds an asymmetric impact of oil prices on economic activities. Burbridge and Harrison (1984), using somewhat different methods and OECD data, find mixed but overall reinforcing evidence of impact based on analysis of the US, Japan, Germany, the UK, and Canada. They find that oil price increases have a sizeable negative impact on industrial production in the US and the UK, but the responses in other countries are small. This study also finds negative relationships between the oil price shock and macroeconomic indicators by using a comprehensive empirical model. Gisser and Goodwin (1986) work on the US economy covering the period 1961Q1–1982Q4. They employ a reduced-form approach to assess the quantitative significance of the impact of crude oil prices on the US economy. They find that crude oil prices have a significant impact on output, even exceeding the impacts of monetary and fiscal policies. Mork and Olsen (1994) examine the correlation between oil price and GDP in seven OECD countries (the USA, Canada, Japan, West Germany, France, the UK, and Norway) over the period of 1967Q3–1992Q4. They find a significant negative correlation between oil price increases and GDP in most of the countries studied. They estimate bi-variate correlations as well as partial correlations within a reduced-form macroeconomic model. The correlations between oil price increases and GDP are found to be negative and significant for most of the countries, but positive for Norway, whose oilproducing sector is large relative to the economy as a whole. The correlations with oil price decreases are mostly positive, but significant only for the USA and Canada. Cunado and Gracia (2005) examine the impact of oil price shocks on economic activities and inflation in six Asian countries, namely Japan, Singapore, South Korea, Malaysia, Thailand, and the Philippines. Using quarterly data from 1975Q1 to 2000Q2 they find that oil prices have a significant impact on both economic growth and inflation, and this result is more significant when oil price is measured in local currencies. They also find evidence of the asymmetric effect of oil prices on economic activities in their study. Although the paper is a brilliant attempt to study Asian economies, which are not given that much attention in previous works in this field, it could have considered other indicators of

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aggregate macroeconomic performance, such as unemployment, interest rates, or exchange rates. Cologni and Manera (2008) investigate the impact of oil prices on inflation and interest rates in a co-integrated vector-autoregreassive (VAR) framework for G-7 countries. Using quarterly data for the period 1980Q1–2003Q4, they find that, except for Japan and the UK, oil prices significantly affect inflation, which is transmitted to the real economy by increasing interest rates. Impulse response function analysis suggests the existence of an instantaneous, temporary effect of oil price change on inflation. Chen and Chen (2007) examine whether there is any long-run equilibrium relation between real oil prices and real exchange rates. Using the monthly panel data of G7 countries over the period 1972M1–2005M10 they find a co-integrating relationship between real oil prices and real exchange rates. This paper is different from other studies in this field (for example Zhou, 1995; Chaudhuri and Daniel, 1998; Amano and Norden, 1998) in that it assesses the role of real oil prices in predicting real exchange rates over long horizons. Panel predictive regression estimates suggest that real oil prices have significant forecasting power for real exchange rates. Lardic and Mignon (2006) study the long-run equilibrium relationship between oil prices and GDP in 12 European countries using quarterly data spanning from 1970Q1 to 2003Q4. This study finds that the relationship between oil price and economic activities is asymmetric; that is, rising oil prices retard aggregate economic activity more than falling oil prices stimulate it. Their results show that, while the standard co-integration between the variables is rejected, there is asymmetric co-integration between oil prices and GDP in most of the participating European countries. This paper makes a significant contribution to the literature on the asymmetric impact of oil price on GDP and it differs from other studies, such as Mory (1993), in that it employs an asymmetric co-integration procedure to capture this asymmetric relation. In contrast to the above studies, which analyze the impact of oil price shocks, papers investigating the impact of oil price volatility on the economies are very limited and have their origin in the increase of oil price volatility from mid-1980s. Lee and Ni (1995) find that oil price changes have a substantial impact on economic activities (notably GNP and unemployment) only when prices are relatively stable, rather than highly volatile or erratic. This indicates a weaker empirical relationship between oil prices and economic activities in the US since the remarkable increase in oil price volatility. In this study the authors utilize a generalized auto-regressive conditional heteroskedasticity (GARCH) model to construct the conditional variation in oil price changes. Ferderer (1996) analyzes US data spanning from 1970M1 to 1990M12 to see whether the relation between oil price volatility and macroeconomic performance is significant. In this study, the oil price volatility is measured by simple standard deviation. Oil price volatility is found to contain significant independent information that helps forecast industrial production growth. The vector auto-regressive (VAR) framework is utilized to analyze the impact of both oil price shock and oil price volatility on fiscal and monetary policy variables like industrial production growth, federal funds rate and, non-borrowed reserves. Evidence is found that oil market disruptions have impact in the economy through both sectoral shocks and uncertainty channels. Further, monetary tightening in response to oil price increase partially explains the output–oil price correlation and the Federal Reserve reacts to the oil price increase as much as it responds to the oil price decreases. The paper concludes that sectoral shocks and uncertainty channels offer a partial solution to the asymmetry puzzle between oil price and output.

123

Guo and Kliesen (2005) look into the impact of oil price volatility on the US economy. Using the measure of realized volatility constructed from daily crude oil future prices traded on the NYMEX, they find that, over the period 1984–2004, oil price volatility has a significant effect on various key US macroeconomic indicators, such as fixed investment, consumption, employment, and the unemployment rate. The findings suggest that changes in oil prices are less significant than the uncertainty about future prices. They also find that standard macroeconomic variables do not forecast realized oil price volatility, which indicates that the variance of future oil prices reflect stochastic disturbances. They conclude that this is mainly driven by exogenous events, like significant terrorist attacks and military conflicts in the Middle East. Some observations can be made from the above discussion. Firstly, oil price shocks have important impact on aggregate macroeconomic indicators, such as GDP, interest rates, investment, inflation, unemployment and exchange rates. Secondly, the impact of oil price changes on the economy is asymmetric; that is, the negative impact of oil price increases is larger than the positive impact of oil price decreases. There have been few academic endeavors made to analyze the impact of oil price volatility per se on economic activities and, more importantly, such studies are conducted almost exclusively in the context of developed countries, especially the USA. Studies analyzing the impact of oil price volatility are now needed for developing countries. In the face of global competition, maintaining economic stability has become the crucial task for policy makers in these countries. Moreover, the economies of developing countries are fundamentally different from those of developed countries. Developing countries are generally characterized by relatively high unemployment, less-developed financial markets, weak infrastructure, etc. Moreover, the dependence of developing countries on oil is forecasted to be increasing over the next couple of decades. Thus, it is of utmost importance to identify what impact oil price volatility has on the economic activities of these countries. As no known studies have examined this aspect in the context of developing countries, this remains an untapped area of serious research. This study intends to make some contribution to an understanding of the issue of oil price volatility and its impact on the real economic activities of one of these developing countries, Thailand.

Data sources and analytical framework Data This paper uses quarterly data from 1993Q1 to 2006Q4 for Thailand. The rationale behind selecting this period is the availability of data. Since the volatility of oil price is calculated on a quarterly basis, the empirical analysis of this study is based on quarterly data. In Thailand, there is no data of all the relevant macroeconomic indicators prior to 1993. Thus this study has to confine its empirical analysis within this range of time. In calculating the quarterly volatility measure, the daily crude oil prices of ‘‘Arab Gulf Dubai FOB $US/BBL’’ are considered and transformed into local prices by adjusting the world oil prices with the respective foreign exchange rates. Time-series data on some macroeconomic variables, growth rate of gross domestic product (GGDP), investment (INV), interest rate (IR), inflation (INF), are obtained from the International Financial Statistics’ (IFS) compact disk (CD) of May 2007 and data on unemployment rates (UR), trade balance (TB), and budget deficit (BD) are collected from the Bank of Thailand. Investment is calculated by the gross

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fixed capital formation as a percentage of GDP. Both the gross fixed capital formation and real GDP data are on national currency in millions. The budget deficit is also taken as a percentage of GPD to measure its share of overall economic activity to capture the impact of subsidization through the oil fund that is operated by the Energy Fund Administration Institute (EFAI) of Thailand, while trade balance is computed by subtracting net exports from net imports and also taken as a percentage of GDP. The interest rate data represents 3-month Treasury bill rate. In the case of unemployment rates, quarterly observations from 1993Q1 to 2000Q4 are obtained by using the Lisman Sandee (1964) method. The measure applied here in constructing quarterly oil price variance is the measure of realized volatility, which is elaborated below.

Methodology This article employs the Granger causality test to examine the causal relationship between oil price volatility and other leading economic indicators of Thailand. Causality in Granger’s (1969) sense is inferred when values of a variable, say, Xt has explanatory power in a regression of Yt on lagged values of Yt and Xt. If lagged values of Xt have no explanatory power for any of the other variables in the system, then Yt is viewed as weakly exogenous to the system. Vector auto-regression (VAR) of the following form is considered for this purpose: Y t ¼ a0 þ

n X

bi Y ti þ

n X

i¼1

Realized oil price variance Based on the nature of data under consideration, various volatility measures, both parametric and non-parametric (such as historical volatility (HS), stochastic volatility (SV), implied volatility (IV), realized volatility (RV), and conditional volatility (CV)) have been suggested in the literature. The parametric models can reveal well-documented time varying and clustering features of conditional and implied volatility. However, the validity of the estimate relies a great deal on the model specifications along with the particular distributional assumptions and, in the instances of implied volatility, another assumption regarding the market price of volatility risk has to be met (Andersen et al., 2001a). This stylized fact is also unveiled in a seminal article by Andersen et al. (2001b), where they argue that the existence of multiple competing parametric models points out the problem of misspecification. Moreover, the conditional volatility (CV) and stochastic volatility (SV) models are hard to adopt in a multivariate framework for most of the practical applications. An alternative measure of volatility, termed as realized volatility, is introduced by Andersen et al. (2001a) and Andersen et al. (2001b, 2003). Furthermore, the theory of quadratic variation suggests that, under appropriate conditions, realized volatility is an unbiased and highly efficient estimator of volatility of returns, as shown in Andersen et al. (2001b, 2003), and Barndorff-Nielsen and Shephard (2002, 2001a). In addition to that, by treating volatility as observed rather than latent, the approach facilitates modeling and forecasting using simple methods based on observable data (Andersen et al., 2003). According to Andersen et al. (2004), realized volatility is the summation of intra-period squared returns

RV t ðhÞ 

1=h X

r ðhÞ2 t1þih

i¼1

where the h-period return (in this study this is daily oil price return) is given by rðhÞ t ¼ logðSt Þ  logðSth Þ and 1/h is a positive integer. In accordance with the theory of quadratic variation, the realized volatility RVt(h) converges uniformly in probability to IVt as h-0, as such allowing for ever more accurate non-parametric measurements of integrated volatility. Furthermore, papers of Zhang et al. (2005) and Aı¨t-Sahalia et al. (2005) state that the realized variance is a consistent and asymptotically normal estimator once suitable scaling is performed. The estimated realized volatility and all macro-variables used in this study are graphically represented below. These figures reveal two important facts; (i) crude oil price has been highly volatile in recent years, particularly in the second half of 1990s and (ii) all the data series portray spikes around the period of the Asian Financial Crisis of 1997–1998.

X t ¼ f0 þ

n X i¼1

li X ti þ mt

(1)

i¼1

ji Y ti þ

n X

Zi X ti þ nt

(2)

i¼1

where n is the number of the optimum lag length. Optimum lag lengths are determined empirically by the Schwarz Information Criterion (SIC). For each equation in the above VAR, Wald w2 statistics are used to test the joint significance of each of the other lagged endogenous variables in equation. In addition, the Wald w2 statistics tell us whether an endogenous variable can be treated as exogenous. Moreover, roots of the characteristics polynomial test is undertaken to confirm whether the VAR system satisfies the stability condition (Fig. 1). The conventional Granger causality test based on standard VAR is conditional on the assumption of stationarity of the variables constituting the VAR. If the time series are non-stationary, the stability condition of VAR is not met, implying that the w2 (Wald) test statistic for Granger causality is invalid. In that case, the cointegration and vector error correction model (VECM) are recommended to investigate the relationship between nonstationary variables. Therefore, it is imperative to ensure first that the underlying data are stationary or I(0). Since there are a number of tests developed in the time-series econometrics to test the presence of unit roots, this paper uses two most popular methods: the Augmented Dickey–Fuller (ADF) test and the Philips-Perron (PP) test. However, in many empirical literature it has been found that both the ADF and PP unit root tests fail to reject null hypothesis of a unit root for many time series. Therefore, the study also employs the Kwiatkowaski–Philips– Schmidt–Shin (KPSS) unit root test to complement the standard unit root testing process since KPSS can make distinction between the series that appear to be stationary, those that appear to be non-stationary having a unit root at their levels, and those that are not sufficiently informative to be certain whether they are either of them. Hence, the combining use of these tests makes it possible to test for both the null hypothesis of nonstationarity and stationarity, respectively. This process of joint use of unit root (ADF and PP) and stationarity (KPSS) tests is known as confirmatory data analysis (Brooks, 2002). However, these standard tests may not be appropriate when the series contain a structural break. To compensate for such a problem, Perron (1989) proposes a procedure that allows an exogenous structural break at time Tb, that is if the time of break is known as a priori. Afterward, Zivot and Andrews (1992) criticize this test for treating the time of the break as exogenous or a priori. Zivot and Andrews (1992), and latter Perron (1997), further develop a procedure that allows endogenous break points in the series under consideration. Following this, a large number of empirical studies have considered the probable existence of structural break(s) in the series under consideration, such as

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Realized Volatility (RV)

125

GDP Growth (GGDP)

0.14

10.0

0.12

7.5

Investment (INV) 1.8 1.6

5.0

0.10

1.4

2.5 0.08

1.2

0.0 0.06 -2.5 0.04

1.0

-5.0

0.02

0.8

-7.5 -10.0

0.00 1994

1996

1998

2000

2002

2004

2006

0.6 1994

1996

Unemployment Rate (UR)

1998

2000

2002

2004

1994

2006

1996

Inflation (INF)

2002

2004

2006

14

2.5 4

2000

Interest Rate (IR)

3.0

5

1998

12

2.0 10

1.5

3

1.0 2

8

0.5

6

0.0

1

4

-0.5 0

2

-1.0 1994

1996

1998

2000

2002

2004

1994

2006

1996

1998

2000

2002

2004

2006

2004

2006

1994

1996

1998

2000

2002

2004

2006

Budget Deficit (BD)

Trade Balance (TB) 15

8

10

4

5 0 0 -4 -5 -8

-10 -15

-12 1994

1996

1998

2000

2002

2004

2006

1994

1996

1998

2000

2002

Fig. 1. Variables used in this study.

Salman and Shukur (2004), Hacker and Hatemi-J (2005), and Salim and Bloch (2007), among others. Breaks in time-series data due to a shock occur either instantaneously or gradually. Instantaneous change to the new trend function is modeled in the Additive Outlier (AO) model and changes that take place gradually are modeled in the Innovational Outlier (IO) model. In the present context it is reasonable to follow the IO model, because policy reforms at macro level do not cause the target variable to respond instantaneously to the policy actions. Also the date of break is not known a priori. Therefore, an IO model, following Perron (1997), is used to test the stationary process with a structural break both in slope and intercept yt ¼ m þ bt þ yDU t þ gDT t þ dDðT b Þt þ ayt1 þ

k X

ai Dyti þ et

(3)

i

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

corresponding trend shift variable; formally ( DU t ¼

1; 0;

( if t4T b t  Tb DT t ¼ otherwise 0; ( 1; if t ¼ T b þ 1 DT b ¼ 0; otherwise

if t4T b otherwise

and yt is any general ARMA process and et a white noise error term. The null hypothesis that a given series is a realization of a time-series process characterized by the presence of a unit root is rejected when the absolute value of the t-statistics for testing a ¼ 1 in (3), denoted by ta, is greater than the critical values. The breakpoint is estimated by the OLS for t ¼ 2, y, T1, thus T2 regressions are run, and the breakpoint is determined by the minimum t statistic on the coefficient of the auto-regressive variable (ta). The truncation lag parameter k is determined using the data-dependent method proposed by Perron (1997). The optimum k(k*)is selected such that the coefficient on the last lag in an auto-regression of order k* is significant and that the last

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

T

Tb

k1

t b^

t y^

t g^

t d^

a^

ta

Inference

RV GGDP INV UR INF IR TB BD DBD

19 20 17 19 21 22 17 20 18

1997Q4 1998Q1 1997Q2 1997Q4 1998Q2 1998Q3 1997Q2 1998Q1 1997Q3

0 5 0 1 1 0 8 7 1

1.45 4.98 0.79 1.59 2.38 4.48 0.93 2.69 1.85

3.26 3.55 6.80 6.35 3.14 7.56 3.67 4.07 1.68

2.43 4.73 2.02 1.23 0.77 1.89 1.51 3.29 2.06

6.14 5.46 4.96 2.56 1.74 6.76 3.01 1.92 1.42

0.183 0.48 0.54 0.09 0.18 0.58 0.01 1.06 1.07

7.56 5.39 6.65 7.78 6.90 8.79 6.031 4.35 14.55

S S S S S S S NS S

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

coefficient in an auto-regression of order greater than k* is insignificant, up to a maximum order kmax (Perron, 1997). Following Lumsdaine and Papell (1997), and Pahlavani (2005), it is assumed that kmax ¼ 8. Impulse response functions trace the responsiveness of the dependent variable in the VAR system to a unit shock in error terms. For each variable from each equation, a unit shock is applied to the error term and the effects upon the VAR over time are noted. If there are g variables in the VAR system, then a total of g2 impulse responses could be generated. However, in accordance with the objective of this paper, the responses generated by the innovation in the realized volatility of oil prices are examined. One limitation with the Granger causality test is that the results are valid within the sample, which is useful in detecting exogeneity or endogeneity of the dependent variable in the sample period, but is unable to deduce the degree of exogeneity of the variables beyond the sample period. To examine this issue the variance decomposition technique is employed. Unlike impulse responses, a shock to the ith variable not only directly affects the ith variable, but it also transmitted to all of the other endogenous variables through the dynamic (lag) structure of the VAR. Variance decomposition separates the variation in an endogenous variable into the component shocks to the VAR. Thus, variance decomposition provides information about the relative importance of each random innovation affecting the variables in the VAR. Sims (1980) notes that, if a variable is truly exogenous with respect to other variables in the system, own innovations will explain all of the variables forecast error variance. From empirical point of view, this study differs from the previous ones in several ways. One, it applies a comparatively new measure of oil price volatility namely the realized oil price variance. Two, it performs a battery of diagnostic tests for identifying unit roots within different time series. Some of these tests have not previously been employed in similar studies; Ng–Perron, DF-GLS and Perron (1997) unit root tests for example. This study remains one of the very few studies in this field as it employs impulse responses and variance decompositions tests based on the results of structural break test.

Analysis of findings Time-series properties of data The tests for unit root are applied to both the original series and to the first differences.1 Findings of ADF and PP tests differ in their results, while KPSS reveals that all the variables are stationary at their levels. However, since these conventional unit 1

Results not reported, will be provided upon request.

root tests may suffer from low power the study further employs two other tests, namely Ng–Perron and Dickey–Fuller GLS (ERS) tests. The results for these tests also confirm that most of the variables are stationary at their levels. Nevertheless, the significant part of all these tests is that they all indicate that realized volatility (RV) is I (0). However, as mentioned earlier, the traditional unit root test cannot be relied upon if the underlying series contains structural break(s). This study uses Perron’s (1997) unit root test, which allows for a structural break and the test results are summarized in Table 1. The test results provide a very interesting fact that for all the variables the dates for structural break are around the Asian Financial Crisis of 1997–1998. Thus, the study employs multiple VAR systems: firstly, a VAR analysis for the whole sample and, secondly, a VAR analysis for the data period after a date beyond the structural breaks, i.e. from 1999Q1 to 2006Q4. The Perron (1997) test results provide evidence of the existence of unit root in the budget deficit when breaks are allowed. However, when the series is differenced once, the variable becomes stationary. Thus, it can be concluded that the underlying data is non-stationary at level but stationary at its first difference. However, the volatility of oil prices is stationary at level. Since there is one non-stationary variable at level the error correction model cannot be implemented in this regard. Hence, the impact analysis needs to be performed in the VAR system where the only non-stationary variable i.e. budget deficit, is first differenced before putting it into the model. Now the concern is whether an unrestricted VAR or a restricted one (to be more specific VARX) is appropriate for this particular study.

Model selection A number of tests are performed to identify the appropriate model for investigating the relationship among the variables under consideration. In selecting the correct form of the model, the first step is to perform the VAR Granger causality/block exogeneity Wald Test. This test investigates whether an endogenous variable can be treated as exogenous. The result of the exogeniety test for realized volatility is presented in Table 2. The statistic in the last row (all) is the w2 statistic for joint significance of all other lagged endogenous variables in the equation, and the table reveals that, in respect of all other variables, realized volatility of oil prices can be treated as an endogenous variable in the model. Moreover, the same test can be employed to identify whether volatility in oil prices Granger causes other variables in the system. The results of the exogeneity test for the other variables are given in Table 3. The VAR Granger causality test indicates that GDP growth, investment, unemployment, and inflation are Granger caused by oil price volatility.

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

Table 2 Test of endogeniety for RV Excluded variable

w2

D.F.

Probability

GGDP INV UR INF IR TB DBD All

0.000340 2.216266 2.186352 1.227465 3.698375 3.712326 0.006277 20.51950

1 1 1 1 1 1 1

0.9853 0.0136 0.0139 0.2679 0.0545 0.0540 0.9368 0.0046

Note: Here RV is dependent variable.

Table 3 Granger causality test Dependent variable

w2

D.F.

Probability

GGDP INV UR INF IR TB DBD

9.365603 4.831090 15.60052 3.700295 0.455655 0.019409 0.384085

1 1 1 1 1 1 1

0.0022 0.0280 0.0001 0.0544 0.4997 0.8892 0.5354

127

statistics for the significance of all the endogenous variables at lag 1 for each equation separately and jointly reveals that the w2 test statistics for lag exclusion is significant, e.g. according to the test the lag length of 1 appears to be appropriate for the VAR system under consideration. The inverse roots of the characteristics auto-regressive (AR) polynomial indicates that the estimated VAR is stable (stationary) if all roots have a modulus less than one and lie inside the unit circle. Since no root lies outside the unit circle and all the modulus are less than one, the VAR model in this regard is stable. Thus, results from all the tests demonstrate that a VAR (1) is appropriate for investigating the relationship between volatility of oil prices and other concerned macroeconomic indicators. The result of the VAR (1) model is presented in Table 4. The coefficients and respective t-statistics of the variables show that the realized volatility of oil prices has a significant negative impact on GGDP and unemployment. Thus, it shows that, in Thailand, oil price volatility negatively affects to GDP growth and it also gives rise to unemployment. Now a critical analysis concerning the sign of relationships and how long it would take for the impact of the volatility to work through the system is warranted. Source of variability The Granger causality test suggests which of the variables in the models have statistically significant impacts on the future values of each of the variables in the system (Brooks, 2002). However, the result will not, by construction, be able to explain the sign of the relationship or how long these impacts will remain effective in the future. Variance decomposition and impulse

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

Table 4 VAR (1) output for Thailand RV

GGDP

INV

UR

INF

IR

TB

100.7969 (32.9366)

0.340719 (0.80765)

13.82964 (3.50140)

12.41577 (6.45440)

4.568806 (6.76838)

3.974950 (28.5316)

DBD

RV(1)

0.016039 (0.15666)

GGDP(1)

1.260000 (0.00068)

0.233898 (0.14325)

0.002400 (0.00351)

0.046667 (0.01523)

0.013307 (0.02807)

0.051741 (0.02944)

0.273436 (0.12409)

0.296684 (0.15442)

INV(1)

0.032923 (0.02212)

8.112659 (4.64947)

0.764016 (0.11401)

0.840708 (0.49427)

0.517018 (0.91113)

8.080000 (0.95545)

5.424502 (4.02764)

7.892113 (5.01213)

UR(1)

0.006491 (0.00439)

1.470879 (0.92298)

0.015807 (0.02263)

0.488116 (0.09812)

0.175170 (0.18087)

0.274910 (0.18967)

0.585587 (0.79953)

1.483793 (0.99497)

INF(1)

0.004181 (0.00377)

0.147161 (0.79335)

0.029002 (0.01945)

0.151961 (0.08434)

0.187564 (0.15547)

0.441827 (0.16303)

1.362374 (0.68725)

2.152068 (0.85523)

IR(1)

0.001620 (0.00084)

0.090718 (0.17710)

0.004775 (0.00434)

0.024487 (0.01883)

0.015629 (0.03471)

0.850663 (0.03639)

0.054799 (0.15342)

0.237514 (0.19092)

TB(1)

0.001501 (0.00078)

0.034675 (0.16377)

0.008827 (0.00402)

0.015312 (0.01741)

0.042119 (0.03209)

0.042678 (0.03365)

0.785600 (0.14187)

0.075512 (0.17654)

DBD(1)

5.540000 (0.00070)

0.555279 (0.14709)

0.004277 (0.00361)

0.021044 (0.01564)

0.037688 (0.02883)

0.064336 (0.03023)

0.046624 (0.12742)

0.051830 (0.15857)

C

0.071668 (0.03399)

18.15349 (7.14526)

0.404566 (0.17521)

1.686756 (0.75959)

0.074393 (1.40021)

1.014942 (1.46833)

7.802119 (6.18963)

13.83383 (7.70260)

Notes: Standard errors are reported in ( ).

22.00455 (35.5058)

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

response functions give this information. In relation to these tests, it is often observed that the results of these tests are sensitive to the ordering of the variables. However, with respect to the study in hand no major difference is seen in changing the order of presentation of the concerned variables. The ordering used for both of the tests, i.e. impulse response functions and variance decomposition is: realized volatility, GDP growth, investment, unemployment rate, inflation, interest rate, trade balance, and budget deficit. Impulse response functions The orthogonalized impulse response functions trace out responsiveness of the dependent variables in the VAR to shocks to each of the variables. For each variable from each equation separately, a unit shock is applied to the error, and the effects upon the VAR system over time are noted. Since the VAR system has eight variables, a total of 64 impulses could be generated. Since the primary aim of the paper is to examine the impact of oil price volatility on the other seven economic indicators, the paper only traces out the responsiveness of the dependent macroeconomic variables in the VAR to shock to realized volatility. The

Response of RV to RV

results of the impulse responses of the variables are presented in Fig. 2. According to the above figures, the impulse responses show that, in most of the cases, realized volatility has its impact on the shorter time horizon and the highest impact would happen to investment and unemployment rate. Variance decomposition Variance decomposition gives the proportion of the movements in the dependent variables that are due to their ‘‘own’’ shocks, versus shocks to the other variables. The results of variance decomposition over a period of a 40-quarter time horizon for different variables are presented in Table 5. As the table below suggests, the variance decomposition results are consistent with the findings of impulse responses. The results of the test reveal that oil price volatility explains a fair portion of innovations in investment and unemployment rate over a longer time period horizon. However, since the Perron (1997) test for structural break indicates the existence of structural break for all the variables in the system, the relationship is further investigated in the vector

Response of GGDP to RV

0.020

2

0.015

1

0.010

0

0.005

-1

0.000

-2

Response of INV to RV 0.04

0.00

-0.04

-0.08

-3

-0.005 5

10

15

20

25

30

35

40

-0.12 5

Response of UR to RV

10

15

20

25

30

35

5

40

Response of INF to RV

10

15

20

25

30

35

40

35

40

Response of IR to RV

0.5

0.4

0.8

0.4

0.3

0.3

0.2

0.2

0.1

0.0

0.1

0.0

-0.4

0.0

-0.1

-0.1

-0.2

0.4

-0.8 -0.2

-0.3 5

10

15

20

25

30

35

40

-1.2 5

Response of TB to RV

10

15

20

25

30

35

40

5

10

15

Response of DBD to RV

3

1.5 1.0

2 0.5 1

0.0 -0.5

0 -1.0 -1

-1.5 5

10

15

20

25

30

35

40

5

10

15

20

25

30

35

40

Fig. 2. Responses from RV, GGDP, INVT, UR, INF, IR, TB and DBD to Cholesky One S.D. Innovations+S.E in RV.

20

25

30

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

S. E.

RV

GGDP

S.E.

RV

INV

S.E.

RV

UR

S.E.

RV

INF

1 10 20 30 40

3.428 4.583 4.602 4.608 4.608

0.318 15.131 15.165 15.183 15.182

99.682 64.230 63.715 63.606 63.594

0.084 0.266 0.281 0.289 0.290

8.269 23.222 22.514 22.389 22.387

91.512 34.498 31.919 31.500 31.289

0.364 0.941 0.987 1.004 1.008

3.472 26.196 25.559 25.335 25.313

85.474 17.374 15.811 15.286 15.157

0.672 0.882 0.922 0.925 0.927

1.887 11.440 12.414 12.411 12.463

74.742 49.466 47.053 46.76536 46.618

Period

S.E.

RV

IR

S.E.

RV

TB

S.E.

RV

DBD

1 10 20 30 40

0.704 2.500 3.491 3.577 3.603

1.619 8.123 14.692 14.943 14.992

86.778 30.293 16.085 15.778 15.624

2.969 6.611 6.766 6.848 6.862

5.178 15.597 15.621 15.731 15.758

82.905 46.778 45.314 45.038 44.938

3.696 4.427 4.428 4.429 4.429

0.917 3.152 3.159 3.162 3.163

46.519 34.972 34.954 34.947 34.945

Note: The decompositions are reported for one-, 10-, 20-, 30 and 40-quarter horizons. Ordering used here is RV, GGDP, INVT, UR, INF, IR, TB, and DBD. However, changing the order did not alter the results to any substantial degree. This is because the Choleski decomposition is used in order to orthogonalize the innovations across equations.

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

w2

D.F.

Probability

GGDP INV UR INF IR TB BD All

7.878130 0.544623 9.290095 1.514428 0.169897 1.363557 0.256577 20.51950

2 2 2 2 2 2 2

0.0195 0.7616 0.0096 0.4690 0.9186 0.5057 0.8796 0.0195

Note: Here RV is dependent variable.

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

w2

D.F.

Probability

GGDP INV UR INF IR TB BD

3.702518 5.038456 0.357929 2.367984 1.547978 2.659912 3.989715

2 2 2 2 2 2 2

0.1570 0.0805 0.8361 0.3061 0.4612 0.2645 0.1360

Banking Crisis of Asia. Moreover, for the data period from 1999Q1 to 2006Q4, the SIC indicates that the appropriate lag length of VAR is 2. The VAR Granger causality/block exogeneity Wald Test table indicates that, in respect of all other variables, realized volatility of oil prices can be treated as an endogenous variable in the model as is the case for the longer period (Table 6). The test for Granger causality for all the other variables when RV is excluded indicates that only investment is Granger caused by oil price volatility when the data set spans from 1999Q1 to 2006Q4 (Table 7). The results of the VAR (2) output for the data period 1999Q1–2006Q4 is presented in Appendix Table A1, and the impulse response functions are given in Fig. 3. The impulse responses for the period after the banking crisis (1999Q1– 2006Q4) show that, in most of the cases, realized volatility has its impact on the shorter time horizon and the highest impact would happen to investment, interest rate, and budget deficit. The results of variance decomposition over a period of a 40quarter time horizon for different variables for the period of 1999Q1–2006Q4 are presented in Table 8. The results of variance decomposition show that, oil price volatility explains a fair portion of innovations in GDP growth, investment, and budget deficit over a longer time period horizon.

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

Conclusions and policy implications

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

This paper empirically examines the impact of oil price volatility on key macroeconomic variables in Thailand by using vector auto-regression systems. The empirical results from the Granger causality test show that there is unidirectional causality running from oil price volatility to investment, unemployment rate, interest rate and trade balance for the whole data period. The impulse responses show that, in most of the cases, realized volatility has an impact on the shorter time horizon, with the highest impact on investment and unemployment rate. Like Cunado and Gracia (2005) and Guo and Kliesen (2005), this study finds that the nature of the relationship between oil price volatility and economic indicators is a short-term one. Results from the impulse response functions and variance decomposition confirm that a fair portion of fluctuations in unemployment rate and investment is explained by oil price volatility. All these findings support the popular notion of the impact of uncertainty on delaying

Model estimation after 1999 As budget deficit series is found to be a I (1) process for the whole period from1993Q1 to 2006Q4, the series from 1999Q1 to 2006Q4 is further investigated by both tests of unit root and test for structural beak. The tests indicate that the budget deficit series as a stationary process at the level after the date of structural break. Thus, a VAR for all the variables in their levels is required to see the actual impact of realized volatility on the macroeconomic activities of Thailand in the recent times, more precisely after the

ARTICLE IN PRESS 130

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

Response of RV to RV

Response of GGDP to RV

0.02

Response of INV to RV

2

0.6

1

0.4

0.01

0.2 0 0.0

0.00

-1 -0.2 -2

-0.01

-0.4

-3 5

10

15

20

25

30

35

40

-0.6 5

Response of UR to RV

10

15

20

25

30

35

5

40

Response of INF to RV

10

15

20

25

30

35

40

35

40

Response of IR to RV

0.8

2 0.12

0.4

0.08

1

0.04

0

0.00 0.0

-0.4

-0.04

-1

-0.08

-2

-0.12 -3 5

10

15

20

25

30

35

40

5

Response of TB to RV

10

15

20

25

30

35

40

35

40

5

10

15

20

25

30

Response of BD to RV

2

0.6 0.4

1 0.2 0

0.0 -0.2

-1 -0.4 -2

-0.6 5

10

15

20

25

30

35

40

5

10

15

20

25

30

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

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

S.E.

RV

GGDP

S.E.

RV

INV

S.E.

1 10 20 30 40

1.798 4.078 5.073 5.589 5.913

3.827 17.765 16.708 16.211 15.970

96.173 66.125 67.734 68.545 68.095

0.456 0.983 1.051 1.104 1.1545

14.510 6.0234 6.2014 6.3788 6.3248

84.647 21.783 19.617 18.129 17.079

0.206 1.6284 1.801 1.827 1.856

Period

S.E.

RV

IR

S.E.

RV

TB

S.E.

RV

BD

1 10 20 30 40

2.772 4.543 5.207 5.544 5.819

75.221 33.689 27.819 25.529 23.801

1.746 3.505 4.315 5.150 6.049

35.649 22.630 18.211 18.079 17.872

0.409 0.844 1.048 1.303 1.624

7.444 17.375 14.177 10.448 8.287

46.594 14.158 9.371 6.137 4.027

2.831 9.889 8.459 8.254 7.958

0.000 5.424 6.427 6.600 5.954

RV 0.836 1.024 0.889 0.971 0.991

UR

S.E.

RV

INF

65.749 27.713 27.126 27.006 28.437

0.0596 0.17089 0.262 0.3684 0.495

5.192 6.412 4.620 4.127 3.842

51.007 48.013 50.372 51.861 52.849

Note: The decompositions are reported for one-, 10-, 20-, 30- and 40-quarter horizons. Ordering used here is RV, GGDP, INVT, UR, INF, IR, TB, and BD. However, changing the order did not alter the results to any substantial degree. This is because the Choleski decomposition is used in order to orthogonalize the innovations across equations.

and reducing investment, which consequently transmits to unemployment through sectoral restructuring and resource reallocation. It can be inferred from the VAR system for the full period that oil price volatility has significant impact on growth, employ-

ment and investment. However, since Perron (1997) test identifies structural breaks in all the variables around 1997–1998 (during the Asian Financial Crisis), the study decomposes the whole time period into two and employs VAR analysis for the period of 1999Q1–2006Q4. The results after the

ARTICLE IN PRESS S. Rafiq et al. / Resources Policy 34 (2009) 121–132

financial crisis show that adverse effect of oil price volatility has been mitigated to some extent. During this period budget deficit has been affected most, which may be due to the change in exchange rate regime that put higher pressure on oil fund. It seems that oil subsidization plays significant role in improving economic performance by lessening the adverse effect of oil price volatility on macroeconomic indicators. The policy implication of this result would be that the government should keep pursuing its policy to stabilize domestic oil

131

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

Appendix For VAR (1) output for 1999Q1–2006Q4 see Table A1.

Thailand

for

the

period

of

Table A1 RV

GGDP

RV(1)

0.176607 (0.26109)

RV(2)

0.570845 (0.35744)

4.517563 (46.1928)

GGDP(1)

0.002041 (0.00177)

0.232980 (0.22849)

GGDP(2)

0.001874 (0.00148)

INV(1)

INV

INF

IR

TB

DBD

1.748280 (3.86086)

0.607179 (1.11819)

39.37850 (52.0271)

43.66341 (32.7704)

14.38598 (7.69104)

2.452231 (5.28554)

2.025462 (1.53080)

59.85878 (71.2254)

31.12911 (44.8629)

3.831760 (10.5291)

0.125123 (0.05794)

0.049746 (0.02614)

0.004445 (0.00757)

0.113808 (0.35232)

0.018680 (0.22191)

0.077189 (0.05208)

0.634877 (0.19083)

0.065793 (0.04839)

0.059071 (0.02184)

0.011536 (0.00632)

0.037956 (0.29424)

0.514596 (0.18534)

0.026430 (0.04350)

0.004188 (0.00828)

0.199843 (1.07019)

0.300970 (0.27137)

0.357581 (0.12246)

0.005581 (0.03547)

0.304139 (1.65015)

1.575878 (1.03938)

0.129416 (0.24394)

INV(2)

0.005463 (0.00876)

1.509865 (1.13189)

0.550350 (0.28701)

0.165882 (0.12952)

0.014027 (0.03751)

0.468762 (1.74528)

1.128605 (1.09931)

0.062441 (0.25800)

UR(1)

0.012968 (0.01315)

1.236178 (1.69912)

0.361000 (0.43084)

0.781499 (0.19442)

0.015431 (0.05631)

0.558082 (2.61990)

0.140399 (1.65020)

0.081181 (0.38729)

UR(2)

0.013883 (0.01146)

1.080146 (1.48098)

0.337705 (0.37553)

0.014773 (0.16946)

0.008116 (0.04908)

1.913536 (2.28354)

0.098160 (1.43834)

0.132217 (0.33757)

INF(1)

0.025648 (0.04418)

4.703893 (5.70943)

0.076855 (1.44773)

0.495949 (0.65329)

0.280802 (0.18921)

11.95022 (8.80346)

2.225445 (5.54505)

1.506360 (1.30139)

INF(2)

0.056236 (0.04629)

1.818193 (5.98183)

0.546661 (1.51680)

1.324780 (0.68446)

0.696311 (0.19823)

4.116152 (9.22347)

2.139267 (5.80961)

0.128571 (1.36348)

IR(1)

0.000138 (0.00132)

0.037615 (0.17113)

0.132176 (0.04339)

0.009829 (0.01958)

0.001258 (0.00567)

0.044974 (0.26386)

0.031565 (0.16620)

0.026240 (0.03901)

IR(2)

0.000630 (0.00160)

0.017428 (0.20660)

0.001401 (0.05239)

0.015473 (0.02364)

0.002622 (0.00685)

0.052030 (0.31856)

0.180121 (0.20065)

0.027026 (0.04709)

TB(1)

0.003541 (0.00304)

0.318590 (0.39324)

0.258664 (0.09971)

0.128737 (0.04500)

0.007185 (0.01303)

0.444279 (0.60634)

0.226425 (0.38192)

0.019350 (0.08963)

TB(2)

0.000833 (0.00285)

0.751676 (0.36869)

0.080683 (0.09349)

0.103622 (0.04219)

0.003647 (0.01222)

0.330218 (0.56849)

0.023471 (0.35807)

0.037011 (0.08404)

BD(1)

0.004656 (0.00931)

0.019620 (1.20259)

0.003840 (0.30494)

0.038453 (0.13760)

0.026735 (0.03985)

0.959234 (1.85429)

0.658712 (1.16797)

0.481852 (0.27412)

BD(2)

0.002426 (0.00832)

0.134277 (1.07473)

0.149265 (0.27252)

0.065791 (0.12297)

0.001447 (0.03562)

0.152756 (1.65715)

0.594786 (1.04379)

0.296999 (0.24497)

0.075894 (0.06187)

8.496136 (7.99610)

1.324581 (2.02756)

1.424487 (0.91494)

0.068964 (0.26499)

2.563754 (7.76589)

3.204182 (1.82261)

C

63.27385 (33.7418)

Notes: Standard errors are reported in ( ).

1.212755 (8.55586)

UR

26.12685 (11.7130)

16.17972 (12.3293)

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