Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach

Nội dung text: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach

1. International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2021, 11(3), 110-120. Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach Doan Van Dinh* Faculty of Finance and Banking, Industrial University of Ho Chi Minh City, Ho Chi Minh, Vietnam. *Email: doanvandinh@iuh.edu.vn & citydinhninh@yahoo.com Received: 03 November 2020 Accepted: 26 January 2021 DOI: ABSTRACT The crude oil price fluctuation investigation is to explore the impact of crude oil price shocks on the countries’ economic growth. The Vector Autoregressive Model (VAR) was applied and the variance decomposition is to analyze the impact of the GDP growth due to the shock of the crude oil price. Besides, nine countries’ data were collected from 1990 to the third quarter of 2020. The countries’ GDP growth was impacted by the crude oil price in 5 years respectively: 44.98%; 40.03%; 31.06%; 32.27%; 33.21%; 36.03%; 27.79%; 15.35%; 40.75% of the following countries are: China; America; Japan; Korea; Singapore; Thailand; Indonesia; Vietnam; Malaysia. This study identifies issues for countries to consider: have a comprehensive solution among macroeconomic policies, monetary policy, fiscal policy and other policies to control and stimulate growth. Keywords: Impulse Response Functions, Oil Price Stabilization Fund, Economic Growth, Cointegration Method, Macroeconomic Policies JEL Classifications: E31, E37, D24 1. INTRODUCTION growth in OPEC countries, which showed oil price fluctuation shock during periods in the global business cycle and financial The widespread COVID-19 epidemic had a strong impact on instability. That affects the relationship between economic the global economy, leading to a drop in world oil prices, and growth and oil in OPEC countries. Studies have analyzed the causing the world economy to be in crisis. The crude oil price impact of crude oil price fluctuation on economic growth, as fluctuation is considered the major cause of the economic crisis oil is the essential energy source for economic growth in each and negative economic growth. However, the impact of oil prices country. Specifically, oil is the input material of many economic on economic growth is different in the literature (Dinh, 2018; sectors. So, the decreased gasoline prices make production Antony et al., 2018; Rebeca and Marcelo, 2006). Studies have costs decrease, leading to lower product costs which enhance analyzed the causes between energy consumption by countries the competitiveness of domestically produced goods. Thereby and the impact of crude oil price fluctuation on economic growth contributing to improving the competitiveness of the economy in seven low-income oil-importing that namely Ethiopia, Gambia, and vice versa, the increase in oil prices makes the production Mali, Mozambique, Senegal, Tanzania and Uganda. The research costs increase, leading to decreased competitiveness of the results show the effect of economic growth and oil price. Evidence economy. Otherwise, the oil price fluctuation impacts the shows a mutual interdependence between energy consumption and exporting and importing countries’ income. In case the oil price economic growth Motunrayo and Nicholas (2020). increases, the oil exported countries have a high profit, but the oil-importing countries have to meet the high cost. In case Also relating to the study of oil prices, Zied et al. (2016) the price falls, the oil-importing countries have an economic studied the relationship between crude oil price and economic advantage. This Journal is licensed under a Creative Commons Attribution 4.0 International License 110 International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021
2. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach Up to now, the world crude oil price is the biggest concern of each 2. LITERATURE REVIEW country because the world’s crude oil is an indispensable source of energy for the country’s economic development. When oil Over the past two decades, countries’ economy was impacted by prices fluctuate, it always becomes the hot spot for each country, the oil price crisis in the world, but the oil price crisis of 2020 including Vietnam. Therefore, the author explores and find out due to the spread of the Covid-19 pandemic is the biggest oil the risk level of oil price fluctuation impacts on economic growth. crisis since the regional war in the year 1991. So, the study of oil The author’s contribution to the article is explored the crude oil prices impact on economic growth has been mentioned by many price fluctuation impact on countries’ economic growth, including researchers (Riadh et al., 2017; Antony et al., 2018; Dinh, 2019a). Vietnam from 1995 to 2020. It is important to consider the Authors Shuddhasawtta et al. (2009) used panel-auto regressive impact of oil prices on the countries’ economic growth to define distributive lag (panel-ARDL) to explore the impact of oil prices impacts that have systemic across countries. Besides, Considering on economic growth in seven low-income African oil-importing the variance change of endogenous variables in oil prices and countries (SSA) in short- and long-term economic growth. The economic growth. The study applied the VAR model to find the oil prices do not have a significant effect on short-term economic impact level of oil price fluctuation for economic growth and growth for these countries but have a significantly negative effect considering these impacts are causality one-way or two-way. It in the long run. However, the short-term coefficients suggest that means that oil price needs to be maintained at a reasonable price oil prices have a significant effect on economic growth in all level to reach good economic growth. If the input fuel price is seven countries. too high, leading to high CPI, the growth possibility goes down. Hence, the article applies the VAR model that has two variables Besides, the study on the relationship between economic growth that are the GDP and crude oil price variables that are endogenous with energy consumption, oil prices, capital and labor in developing variable. To keep the GDP growth target, the countries force countries and industrial infrastructure. The energy consumption on input-fuel valorization or fiscal and monetary policies make relative to the oil price, developing countries almost used on stabilization policy reasonably. Therefore, the empirical results on crude oil consumption for economic growth. So, the oil price the crude oil price and economic growth showed that they had a impact on economic growth strongly Muhammad et al. (2017). close relationship and their relationship is negative and positive. The dynamic relationships between oil price and economic growth Besides, they are causal relationship two-way. It means that the were applied by the bounds testing approach to cointegration and economic growth displays a causal relationship with the crude oil the ARDL. The crude oil plays an important role in each country’s price and vice versa. economic development in many sectors because the increased oil price makes the high input cost of enterprises, households and 1.1. Contributions economic organizations, leading to the oil price fluctuation impact The study evaluated whether the crude oil price impacts on GDP on economic growth. So, several studies focused on these impacts growth or not, and how does it impact? The results of the paper (Dinh, 2020a; Mehmet et al., 2017; Mehrara, 2007). demonstrate that the change in input-fuel price impacts on the GDP growth and find out the fittest forecast model of economic growth. The evidence showed a negative relationship between oil prices These contributions play an important role in planning, orienting or and economic development. This relationship diminishes over setting an appropriately adjusted oil valorization policy to promote time because of oil alternatives and government preventive economic growth through goals as follows: measures against the sudden increase in oil price shock. Besides, • Explored the relationship between crude oil prices and the literature showed that fluctuations in oil prices affect economic countries’ GDP growth at different levels (Melike and ệzgỹr, 2015; Niaz and • Found out the impact of crude oil on countries’ economic Josộ, 2013; Dinh, 2020b). The literature identified the long- growth term relationship between oil prices and economic growth. This • Evaluated the endogenous variables influence of countries’ relationship is analyzed based on data from the US economy GDP and the impact of crude oil prices on their GDP countries, G7 economies, Europe and the Euro area. Besides, the • Compared impact level of oil prices on countries’ GDP study has applied the VAR model to determine the disproportionate • Identified the cause of oil price impact on countries’ GDP cointegration between oil price and GDP Sandrine and Valộrie • Lessons learned of nCovid impact from countries including (2008). Another study analyzed the effects of oil price volatility Vietnam. on GDP and applied automatic vector regression (VAR), Granger causality test and impulse response functions to analyze variance. The remainder of the paper is organized as follows: Section 2 Finding out that oil price volatility significantly impacts on literature review; Section 3 explains the data and methodology of economic growth Shuddhasawtta et al. (2009). economics; Section 4 summarizes the results; Section 5 discusses the findings and the last section gives conclusions. The crude oil is directly related to the production process, and it has a significant effect on the consumer price index through increased The main section of an article is started with an introductory commodity prices that lead to inflation. Besides, employment, section which provides more details about the paper’s purposes, output and inflation are impacted by the increased oil price shock. motivation, research methods, and findings. The introduction is It makes increased production costs. The inflationary pressures relatively nontechnical, yet clear enough for an informed reader can lead to reduced demand and this leads to output cuts, leading to understand the manuscript’s contribution. to unemployment (Dinh, 2019b; Jungwook and Ronald, 2008) International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 111
3. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach applied variance decomposition analysis to estimate oil price shock correlation in individual error terms. A p th-order VAR is also and oil price volatility relate to actual stock returns. The oil price called a VAR with p lags. The process of choosing the maximum volatility impacted on stock markets and investors’ profit when lag p in the VAR model requires special attention because inference increased volatility in oil prices can reduce the real return of stocks. is dependent on correctness of the selected lag order. Endogenous variable impact analysis is to assess the volatility of oil prices itself and economic growth, the events of 2008 showed Thus, the relationship between the crude oil price and countries’ that the volatility of oil prices is a source of exogenous volatility, GDP growth, including Vietnam’s GDP growth is considered but influences Financial benefit is an endogenous variable. Besides, whether they have a positive or negative and linear or nonlinear the oil-rich countries have the impact of their financial crisis when relationship or not and find the response of oil price impulses to their economic growth has depended on continuously falling oil economic growth. So, the study used the following econometrics prices (Uchechukwu et al., 2019; Dinh, 2020c). model for analysis. To determine the cointegration model, it is tested by first unit root to determine if it is lag and trend. Hence, Almost the literature applied the VAR model to estimate the the model is tested by unit root as follows: impact of crude oil price, economic growth and other analysis indexes. So, this model is the most common estimation method Unit root test is a commonly used test to test whether a time series for economic growth forecast models. Regression analysis is like is stationary or not. Dickey and Fuller (1981) introduced Dickey other deductive methods. The objective of the paper is to collect and Fuller (DF) tests and extended Dickey and Fuller (ADF) tests. a random-walk sample from countries’ GDP growth and crude oil This study applies the ADF test to perform unit root test Doan Van price to estimate their characteristic by Var model. Dinh (2020a) (2019c). Specifically, according to Dickey and Fuller (1981) the ADF extended unit test model has the form: 3. METHODOLOGY n 01Crudeoilprice    1 Crudeoilpricet t B jCrudeoilpricet j t Applied the empirical method is to investigate the effect of the j1 dynamic relationship between the oil price shock and countries’ economic growth. Therefore, the paper applied the vector (2) automatic regression (VAR) model to establish the forecast model. And The VAR model has been frequently used to estimate the impact n  01Crudeoilprice   of an oil price shock on economic activity. Besides, the advantage Crudeoilpricet tiB j1 of this model is the ability to analyze dynamic relationships   jCrudeoilpriicetj t (3) between endogenous economic variables. Applied the VAR Model to systematically analyze equations because each equation is The GDP variable similarly is written above. represented for each variable in the model as a linear equation for its lag value and its lag of all other variables. Where: ∆GDPt=GDPt–GDPt–1; GDPt: time-series data over time; n: A VAR model is a set of k variables, which is called endogenous stationary time series; εt: white noise variables, over the same sample period (t = 1, , T), that is a linear function. The variables are collected in a k-vector ((k ì 1)-matrix) The model (2) is different from the model (1), where there is an yt and yi,t is the observation at time t of the ith variable. So, the additional trend variable in time t. The trend variable is a value ith variable is GDP, then yi,t is the value of GDP at time t, and the from number 1 to number n. White noise is the term for random ith variable is the crude oil price, then is the value of crude oil errors, the assumption that it has an average of zero, the variance price at time s. is constant and non-correlation. A p-th order VAR denoted VAR(p), is The results of the ADF test are often very sensitive to the choice of the length of the stationary n, so the Akaike’s Information ycttAy11Ay22tp Aytp  et or Criterion (AIC) Akaike (1973) is used to select the optimal k for the ADF model. Specifically, the value of n is chosen such that p the minimum AIC. yc Ay �e (1) t Bt1 ptpt Testing hypothesis: where the observation yt–i (i periods back) is called the i-th lag of H0: β = 0 (GDPt, Crude oilt are the non-stationary data time-series) y, c is a k-vector of constants (intercepts), Ai is a time-invariant H1: β <0 (GDPt, Crude oilt are the stationary data time-series). (k ì k)-matrix and et is a k-vector of error terms satisfying. Tests for Cointegration: Tests for cointegration identify stable, long- ' run relationships between sets of variables. However, Rao (2007) E(e )=0 every error term has mean zero; Ee()e  the t tt notes that if the test fails to find such a relationship, it isn’t proof contemporaneous covariance matrix of error terms is Ω (a k ì k that one doesn’t exist it only suggests that one doesn’t exist. Three ' 0 positive-semidefinite matrix); Eette k  or any non-zero k of the most popular tests are Engle-Granger, Phillips–Ouliaris and — there is no correlation across time; in particular, no serial Johansen test. However, the Johannsen’s test is applied because it 112 International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021
4. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach is another improvement over the Engle-Granger test. It avoids the Advantages of the VAR model as follows: This method fits with issue of choosing a dependent variable as well as issues created the research; It doesn’t need to worry about determining which when errors are carried from one step to the next. As such, the test variables are endogenous or are exogenous. Because all variables can detect multiple cointegrating vectors Dinh (2019a). of the VAR model are endogenous; Sample size fit to the model estimation; the method can be applied to equations. The equation OLS is written: εt=GDPt–β2 Crude oilt where β2 is a stationary process. Let GDPt, and Crude oilt be cointegrated if 3.2. Data Analysis there exists a vector when GDPt and Crude oilt are in equilibrium. This paper uses monthly crude oil price data from January 1990 to The reason for unit roots and cointegration tests is to avoid the September 2020 for nine Countries including Thailand, Vietnam, spurious regression. Singapore, Indonesia, Malaysia, Japan, Korea and America. Simultaneously, data on economic growth rates of nine countries 3.1. Tests for Cointegration is also collected during this period. The rationale for selecting Tests for cointegration identify stable, long-run relationships this stage is the appropriateness of the data because crude oil data between sets of variables. However, Rao (2007) notes that if the had no relevant data before the period 1990. Therefore, this study test fails to find such a relationship, it isn’t proof that one doesn’t limited the empirical analysis to this time series. Since the volatility exist it only suggests that one doesn’t exist. Three of the most of oil prices is calculated monthly, so the empirical analysis popular tests are Engle-Granger, Phillips–Ouliaris and Johansen estimated on a yearly mean of crude oil price to correspond to test. However, the Johannsen’s test is applied because it is another countries’ annual GDP growth indices. Time series data on gross improvement over the Engle-Granger test. It avoids the issue of domestic product (GDP) growth rate variables are collected from choosing a dependent variable as well as issues created when the World Bank The World Bank Group (2020), and data crude oil errors are carried from one step to the next. As such, the test can price is collected from Federal Reserve Economic Data (2020). detect multiple cointegrating vectors Dinh (2019a). The measure used here to construct the annual oil price variance is a measure of the volatility taken here, which is detailed below. th The i variable is GDP, then Yi,t is the value of GDP at time t, and th the i variable is crude oil price, then Yi,t is the value of crude oil price at time t. The Var model is considered as follows: 4. RESULTS n n GDPG10DP  t BBj11iti j Based on the nature of the data to consider various variability measures of crude oil price and GDP variables and the data of  Crudeoilprice U1 (4) jt jt the goodness-fit model ensured that the data is stationary or I (0). n Therefore, the data is done by a test of econometrics over the time  Crudeoilpricet 0 B jtCrudeoilprice  j series to check for the presence of unit-roots. Thus, this paper uses j1 the two most common methods: The Augmented Dickey-Fuller n test (ADF) to select optimum lag (Table 1).  B itGDP i U2tt (5) j1 There are many methods to select lag for a VAR model but the study applied the VAR lag Order Selection Criteria method to find the Where: U1t and U2t are white noise. optimum lag for the model such as Akaike Information Criterion As mentioned above, the major objective of the paper is to analyze (AIC), Schwarz Criterion (SC) and Hannan Quinn (HQ). However, and evaluate the VAR model of the crude oil price and GDP growth The AIC was selected to find the optimum lag of model (Table 2). rate as well as optimal lag, impulse response and the goodness-fit VAR model. It is known, the multivariate LM test statistics for residual serial correlation up to the specified order. A Breusch-Godfrey test To consider the VAR Granger Causality from (4) and (5) equations, statistic for autocorrelation at lag order h is computed by running the hypothesizes as follows: an auxiliary regression of the residuals ut on the original right-hand regressors and the lagged residual ut–h, where the missing first h The hypothesis H1:∑αi=0; ∑βj≠0; the economic growth rate causes the crude oil price (GDP → Crude oil price), one-way Granger. Table 1: Selected time-series stationary at I (0) and I (1) Variables I(0) Prob.* I(1) Prob.* The hypothesis H2:∑αi≠0; ∑βj=0; the crude oil price causes the Log (Crude oil) 0.6697 0.0022 economic growth rate (Crude oil price → GDP), one-way Granger China’s GDP 0.4907 0.0079 America’s GDP 0.1677 0.0001 The hypothesis H :∑ α ≠0; ∑β ≠0; both the crude oil price and the Japan’s GDP 0.0008 3 i j Korea’s GDP 0.003 economic growth rates cause interactions (Crude oil price ↔ GDP), Singapore’s GDP 0.0047 two-way Grange, where the arrow indicates causality. Thailand’s GDP 0.0409 0.0000 Indonesia’s GDP 0.0082 The Granger causality test assumes that the relevant information Vietnam’s GDP 0.2542 0.0201 to predict individual variables, that are the GDP and Crude oil Malaysia’s GDP 0.0021 price, is only included in the time series data for these variables. Source: Author’s analysis. *, Statistical significance at 1%, 5% International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 113
5. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach values of ut–h are filled with zeros. Besides, The Breusch–Godfrey cointegration tests is to analyze non-stationary time series processes test is a test for autocorrelation in the errors in a regression model that have variances and means that vary over time. In other words, Asteriou and Hall (2011) (Table 3). the method allows estimating the long-run parameters or equilibrium in systems with unit root variables Rao (2007) (Table 4). Johansen’s test is another improvement over the Engle-Granger test. It avoids the issue of choosing a dependent variable as well as The goal of variance decomposition or forecast error variance issues created when errors are carried from one step to the next. As decomposition (FEVD) is used to aid in the interpretation of a such, the test can detect multiple cointegrating vectors. The goal of vector autoregression (VAR) model once it has been fitted. The Table 2: VAR lag order selection criteria of countries’ GDP and log (crude oil price) Variables Lag FPE AIC SC HQ Japan’s GDP 0 0.054178* 2.760190* 2.856966* 2.788058* Korea’s GDP 0 0.122866* 3.579018* 3.675795* 3.606887* Singapore’s GDP 0 0.154191* 3.806116* 3.902893* 3.833984* China’s GDP 1 0.051637* 2.710751* 2.993640* 2.799348* America’s GDP 1 0.032247* 2.239952* 2.522840* 2.328549* Thailand’s GDP 1 0.211898* 4.122455* 4.407927* 4.209727* Indonesia’s GDP 1 0.187586* 4.000585* 4.286058 4.087857* Vietnam’s GDP 1 0.020801* 1.801530* 2.084419* 1.890127* Malaysia’s GDP 1 0.150785* 3.782204* 4.067676 3.869475 Source: Author’s analysis Table 3: VAR residual serial correlation LM tests of countries’ GDP and log (crude oil price) Variables Lag LRE* stat df Prob. Rao F-stat df Prob. China’s GDP 1 2.159949 4 0.7064 0.54083 (4, 46.0) 0.7065 2 3.633423 4 0.4579 0.924296 (4, 46.0) 0.4581 America’s GDP 1 2.159949 4 0.7064 0.54083 (4, 46.0) 0.7065 2 3.633423 4 0.4579 0.924296 (4, 46.0) 0.4581 Japan’s GDP 1 1.091864 4 0.8956 0.270286 (4, 46.0) 0.8956 2 5.318306 4 0.2562 1.377769 (4, 46.0) 0.2564 Korea’s GDP 1 4.961836 4 0.2912 1.280467 (4, 46.0) 0.2914 2 4.769978 4 0.3117 1.228403 (4, 46.0) 0.3119 Singapore’s GDP 1 1.27619 4 0.8654 0.316537 (4, 46.0) 0.8655 2 3.724329 4 0.4446 0.94835 (4, 46.0) 0.4448 Thailand’s GDP 1 0.463623 4 0.9769 0.114001 (4, 46.0) 0.977 2 1.144142 4 0.8872 0.283385 (4, 46.0) 0.8873 Indonesia’s GDP 1 1.953358 4 0.7443 0.48802 (4, 46.0) 0.7444 2 2.808029 4 0.5904 0.70801 (4, 46.0) 0.5906 Vietnam’s GDP 1 0.824012 4 0.9352 0.203012 (4, 40.0) 0.9352 2 0.721446 4 0.9487 0.17752 (4, 40.0) 0.9487 Malaysia’s GDP 1 1.380745 4 0.8475 0.342853 (4, 46.0) 0.8476 2 4.529337 4 0.3391 1.163399 (4, 46.0) 0.3393 Source: Author’s analysis. Null hypothesis: No serial correlation at lag h Table 4: Johansen cointegration test of countries’ GDP and log (crude oil price) Variables Hypothesized No. of CE(s) Trace statistic 0.05 - Critical value Prob. China’s GDP None* 28.78555 15.49471 0.0003 At most 1* 13.2738 3.841466 0.0003 America’s GDP None* 28.78555 15.49471 0.0003 At most 1* 13.2738 3.841466 0.0003 Japan’s GDP None* 27.40525 15.49471 0.0005 At most 1* 8.86543 3.841466 0.0029 Korea’s GDP None* 22.52619 15.49471 0.0037 At most 1* 6.078504 3.841466 0.0137 Singapore’s GDP None* 22.55684 15.49471 0.0037 At most 1* 6.967873 3.841466 0.0083 Thailand’s GDP None* 28.0727 15.49471 0.0004 At most 1* 11.54715 3.841466 0.0007 Indonesia’s GDP None* 25.21873 15.49471 0.0013 At most 1* 7.24584 3.841466 0.0071 Vietnam’s GDP None* 24.03182 15.49471 0.002 At most 1* 7.026184 3.841466 0.008 Malaysia’s GDP None* 27.21554 15.49471 0.0006 At most 1* 6.326749 3.841466 0.0119 Source: Author’s analyses. sTrace test indicates 2 cointegrating eqn(s) at the 0.05 level 114 International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021
6. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach variance decomposition indicates the amount of information each Based on AIC criteria, it shows that the variable crude oil price variable contributes to the other variables in the autoregression. has different impacts on the economic growth of countries. Some It determines how much of the forecast error variance of each of countries are affected by oil price immediately when it’s optimal the variables can be explained by exogenous shocks to the other lag 0, but some countries are affected by oil price after 1 year variables Lỹtkepohl (2007) (Table 5). when it’s optimal lag 1. Specifically, countries’ economic growth includes China’s GDP America’s GDP Thailand’s GDP Indonesia’s It is known that impulse response analysis is an important step in GDP Vietnam’s GDP and Malaysia’s GDP that has the optimal lag econometric analysis. It used in the automatic vector regression at lag 1 with the AIC value of 2.710751 * - China’s GDP, 2.239952 model. Besides, the main purpose of this test is to describe the * - America’s GDP, 4.122455 * - Thailand’s GDP, 4.000585 * - development of model variables in response to a single shock Indonesia’s GDP, 1.801530 * - Vietnam’s GDP and 3.782204 * in one or more variables. So, the impulse response test allows - Malaysia‘s GDP. For the remaining countries with optimal lag finding the transmission of a single shock in another noisy system at lag 0 such as AIC of 2,760190 * - Japan’s GDP, 3.579018 * - of equations. Thus, it makes them a very useful tool in evaluating Korea’s GDP, 3.806116 * - Singapore’s GDP (Table 2). economic policies (Figure 1). Test residuals from the model to consider in regression analysis The inverse roots of the characteristic AR polynomial test are and the test statistic is derived from these. The null hypothesis is to estimate the VAR model stable (stationary). It means that, that there is no serial correlation of any order up to the p. So, the if all roots have modulus that is less than one and lie inside the test residuals are based on the LM test for serial correlation. The unit circle, the estimated VAR model is stable, otherwise, if the residual correlation test results show that all variables have no estimated VAR is not stable, certain results are not valid (Figure 2). serial correlation at lag 1 and lag 2 when Prob- value is greater than alpha 0.05 (Table 3). The above results are the basis for analyzing the impact of crude oil prices on GDP of each country and vice versa. Besides, based Cointegration is that there exist close relationships between on empirical research results as a basis for each country to refer variable factors in the model. Therefore, the paper applied the to the macro policymakers. Johansen Cointegration Test to find the relationship between the countries’ GDP variables and oil crude price variable. Consider 5. DISCUSSION two trace statistic values (Trace) and 5% - critical value (value). If value Absolute Critical Value, the data years as follows: is stationary. Besides, the stationary time series are also considered by the P-value test. If P-value < tα (1%, 5% and 10%), the time The results showed that the endogenous variable of economic series is not stationary. So, all data series is stationary when it’s growth fluctuates at 95%, the crude oil price variable is influenced P-value < tα (1%, 5% and 10%) (Table 1). by the economic growth impact that is about 5%. International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 115
7. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach Table 5: Variance decomposition using Cholesky (d.f) factor Variance decomposition of China’s GDP Variance decomposition of D (log crude oil price) Period S.E. D (GDP) D (log crude oil price) S.E. D (GDP) D(log crude oil price) 1 1.92 100.00 0.00 0.12 40.01 59.99 2 2.04 95.23 4.77 0.12 46.13 53.87 3 2.05 95.23 4.77 0.12 46.23 53.77 4 2.05 95.20 4.80 0.12 46.26 53.74 5 2.05 95.20 4.80 0.12 46.26 53.74 Variance Decomposition of America’s GDP Variance Decomposition of D (log crude oil price) Period S.E. D (GDP) D (log crude oil price) S.E. D (GDP) D(log crude oil price) 1 1.96 100.00 0.00 0.12 37.20 62.80 2 2.15 99.26 0.74 0.12 38.45 61.55 3 2.22 97.48 2.52 0.13 41.35 58.65 4 2.26 97.15 2.85 0.13 41.37 58.63 5 2.26 96.88 3.12 0.13 41.79 58.21 Variance decomposition of China’s GDP Variance decomposition of D (log crude oil price) Period S.E. D (GDP) D (log crude oil price) S.E. D (GDP) D(log crude oil price) 1 1.92 100.00 0.00 0.12 40.01 59.99 2 2.04 95.23 4.77 0.12 46.13 53.87 3 2.05 95.23 4.77 0.12 46.23 53.77 4 2.05 95.20 4.80 0.12 46.26 53.74 5 2.05 95.20 4.80 0.12 46.26 53.74 Variance decomposition of Korea’s GDP Variance decomposition of D (log crude oil price) Period S.E. GDP D (log crude oil price) S.E. GDP D(log crude oil price) 1 3.43 100.00 0.00 0.12 32.03 67.97 2 3.52 95.92 4.08 0.12 32.32 67.68 3 3.53 95.39 4.61 0.12 32.33 67.67 4 3.53 95.36 4.64 0.12 32.33 67.67 5 3.53 95.35 4.65 0.12 32.33 67.67 Variance decomposition of Singapore’s GDP Variance decomposition of D (log crude oil price) Period S.E. GDP D (log crude oil price) S.E. GDP D(log crude oil price) 1 4.17 100.00 0.00 0.12 31.71 68.29 2 4.35 94.95 5.05 0.12 33.54 66.46 3 4.37 94.10 5.90 0.12 33.61 66.39 4 4.37 94.03 5.97 0.12 33.61 66.39 5 4.37 94.03 5.97 0.12 33.60 66.40 Variance decomposition of Thailand’s D(GDP) Variance decomposition of D (log crude oil price) Period S.E. D(GDP) D (log crude oil price) S.E. D(GDP) D(log crude oil price) 1 4.20 100.00 0.00 0.11 23.53 76.47 2 4.61 93.03 6.97 0.12 38.80 61.20 3 4.64 92.96 7.04 0.12 39.18 60.82 4 4.65 92.88 7.12 0.12 39.31 60.69 5 4.65 92.88 7.12 0.12 39.34 60.66 Variance decomposition of Indonesia’s GDP Variance decomposition of D (log crude oil price) Period S.E. GDP D (log crude oil price) S.E. GDP D(log crude oil price) 1 3.81 100.00 0.00 0.11 24.37 75.63 2 3.99 99.93 0.07 0.12 27.19 72.81 3 4.00 99.91 0.09 0.12 28.92 71.08 4 4.00 99.91 0.09 0.12 29.23 70.77 5 4.00 99.91 0.09 0.12 29.26 70.74 Variance decomposition of Vietnam’s D(GDP) Variance decomposition of D (log crude oil price) Period S.E. D(GDP) D (log crude oil price) S.E. D(GDP) D(log crude oil price) 1 1.17 100.00 0.00 0.12 8.98 91.02 2 1.18 99.56 0.44 0.12 9.76 90.24 3 1.26 93.46 6.54 0.13 18.84 81.16 4 1.26 92.66 7.34 0.13 18.77 81.23 5 1.28 91.76 8.24 0.13 20.40 79.60 Variance decomposition of Vietnam’s D(GDP) Variance decomposition of D (log crude oil price) Period S.E. GDP D (log crude oil price) S.E. GDP D(log crude oil price) 1 3.83 100.00 0.00 0.12 41.26 58.74 2 3.89 97.68 2.32 0.12 40.81 59.19 3 3.90 97.15 2.85 0.12 40.60 59.40 4 3.91 97.02 2.98 0.12 40.55 59.45 5 3.91 96.98 3.02 0.12 40.54 59.46 Source: Author’s analyses. Cholesky Ordering: (Variables log (crude oil price and countries’ GDP)) 116 International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021
8. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach Figure 1: Response of crude oil price to countries’ GDP innovation using Cholesky (d.f. adjusted) factors The endogenous variable of crude oil price fluctuates around 53%, 5.2. In America China’s growth variable was impacted by the crude oil price is America’s economic growth endogenous variable and crude oil about 46%. Thus, the price of crude oil has a significant impact prices are fluctuations at 99.26% and 61.55% in the 2nd year, on China’s economy. after that it is going down up to the 5th year. As well, America’s International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 117
9. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach Figure 2: Inverse roots of AR characteristic polynomial countries’ GDP and crude oil price economic growth is impacted by crude oil prices at 37.20% in the for 5 years. It showed a significant impact on the endogenous first and 38.45% in the 2nd year, and then it is also stable from the variable and the other variables in the model. third year to the 5th year. It shows that the fluctuation of variables is stable for the final 3 years. 5.4. In Korea Korea’s economic growth endogenous variable and crude oil 5.3. In Japan prices are fluctuations at 95% and 68%, Korea’s economic growth Japan’s economic growth endogenous variable and crude oil prices is impacted by crude oil prices at 32%, it is stable for 5 years. It are very high volatility at 99% and 68.9%, Japan’s economic also showed a significant impact on the endogenous variable and growth is impacted by crude oil prices at 31%, it is stable the other variables in the model. 118 International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021
10. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach 5.5. In Singapore countries not only crude oil imports but also exports of crude oil Singapore’s economic growth endogenous variable and crude oil such as Vietnam or Indonesia etc. The comparison result between prices are fluctuations at 94.9% and 68.3% in the nd2 year, after that it Vietnam’s GDP and the countries’ GDP shows that Vietnam’s goes down at 94,03% and 66.04% in the 5th year. Besides, Singapore’s GDP is least impacted by crude oil prices. So, Vietnam has an economic growth was impacted by crude oil prices at 33.60%, it achievement by an appropriate macro policy such as implementing is not stable for 5 years. It also showed a significant impact on the an oil-to-oil stabilization fund. Thus, Vietnam has had a strategy endogenous variable and the other variables in the model. to stabilize petroleum prices for a long time. The empirical result is also consistent with relevant literature Dinh (2020d). 5.6. In Thailand Thailand’s economic growth endogenous variable and crude 6. CONCLUSION oil prices are fluctuations at 93.03% and 76.5% in the 2nd year, th after that it goes down at 92.88% and 60.66% in the 5 year. But The study applied the VAR model to explore the fluctuations Thailand’s economic growth is impacted by crude oil prices at of countries’ economic growth endogenous variables and the nd th 23.53% in the 2 year and then it goes up at 39.34% in the 5 shock from crude oil prices. The crude oil is the input material of year, it is not stable for 5 years. many economic sectors, so the decrease in gasoline prices make production costs decrease leading to decreased produced costs 5.7. In Indonesia that improve the competitiveness of domestically produced goods, Indonesia’s economic growth endogenous variable and crude oil contributes to improving the competitiveness of the economy and prices are high fluctuations at 99.93% and 75.63% in the 2nd year, th GDP growth. Based on the input costs of the product, countries can after that, crude oil price variable goes down at 70.74% in the 5 make reasonable policy to grow their economies. If the crude oil year. Besides, Thailand’s economic growth is impacted by crude price goes down, the crude oil-importing countries have benefit. oil prices at 24.37% in the 2nd year, and then it goes up at 29.26% th If the price of crude oil increases, the importing countries have in the 5 year, the fluctuation of variables is not stable for 5 years. not to benefit. According to (Wikipedia), oil-importing countries including the United States’ rank second, China’s rank 1st, 5.8. In Vietnam Malaysia’s rank 33rd, Indonesia’s rank 23rd, Vietnam’s ranked 32nd, Vietnam’s economic growth endogenous variable and crude oil Thailand’s rank 14th and Japan’s rank fourth. The data based on the prices are also high fluctuations at 99.56% and 90.24% in the nd2 list of 84 oil-importing countries in the world. So, the United States year, after that, it goes down at 91.76 and 79.60% in the 5th year. and China don’t have benefit when the crude oil price increases. Besides, Vietnam’s economic growth is very lowly impacted by crude oil prices at 8.98% in the 1st year, and then it goes up at 20.40% The above experimental results are the key for Governments to have in the 5th year, the fluctuation of variables is not stable for 5 years. suitable macro policies for economic development. Especially the context that all economies in the world are being affected by the 5.9. In Malaysia COVID-19 epidemic. So, social-distancing almost makes social- Malaysia’s economic growth endogenous variable and crude oil activity to interrupt lead to reduced fuel consumption, so the crude oil prices are fluctuations at 97.68% and 59.74% in the nd2 year, after price falls. It is also the opportunity for importing countries to increase that it is stable up to the 5th year. As well, Malaysia’s economic oil imports and reserve crude oil to stabilize domestic oil prices. growth is impacted by crude oil prices at 41% in the first and 40.81% in the 2nd year, and then it is also stable up to the 5th year. The maintenance of the Petroleum Price Stabilization Fund It shows that the fluctuation of variables is stable for 5 years. is essential to stabilize the domestic petroleum price, avoid an increased shock and cause stability the national economy. Results show that countries’ GDP includes China, America, Thailand, Therefore, in case the world oil price changes, the domestic oil Indonesia, Vietnam and Malaysia will not be affected by oil price price is adjusted to stable the socio-economics. immediately, but it will affect countries’ GDP next year. It means that when the world oil price fluctuates, it does not have an immediate impact on these countries’ economic growth, but the crude oil price 7. Acknowledgements fluctuation will affect economic growth next year. Countries’ GDP include Japan, Korea and Singapore are affected by crude oil prices The author would like to thank a Board of Rectors at the Industrial immediately when crude oil prices increase or decrease. Besides, University of HCM City for finance funding. Similarly, the author the countries’ GDP is highly affected by the price of crude oil, that would like to thank the Economic Subcommittee Council (ESC- is China at 46.26% and the US at 41.79%, the countries’ GDP is IUH) for their exceptionally insightful comments which helped to significantly affected, that is Thailand at 39.34%, Singapore at improve this article which relates to topic “the impact of the world 33.6%, Korea at 32.33% and Japan at 31%, the countries’ GDP is crude price on the economic growth of Vietnam and other countries”. low impacted, that is Vietnam at 20.4% and Indonesia at 29.6%. REFERENCES Why is there a different impact of crude oil price on countries’ GDP? Antony, K., Charles, C.N., Kevin, W. (2018), Effect of crude oil prices The impact of oil prices on GDP depends on each country’s on GDP growth and selected macroeconomic variables in Kenya. macro policy and its economic growth. Besides, there are some Journal of Economics and Business, 1(3), 282-298. International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021 119
11. Dinh: Impact of Crude Oil Price on Countries’ and Vietnam’s GDP Growth: Variance Decomposition Approach Asteriou, D., Hall, S.G. (2011), The Breusch-Godfrey LM Test for 319-336. Serial Correlation. 2nd ed. New York: Palgrave Macmillan: Applied Mehrara, M. (2007), Energy consumption and economic growth: The Econometrics. case of oil exporting countries. Energy Policy, 35(2007), 2939-2945. Dickey, D., Fuller, W.A. (1981), Likelihood ratio statistics for Melike, B., ệzgỹr, E. (2015), An investigation of the relationship between autoregressive time series with a unit root. Econometrica, 49(4), the biomass energy consumption, economic growth and oil prices. 1057-1072. Procedia Social and Behavioral Sciences, 210(2), 20-212. Dinh, D.V. (2018), World crude oil prices impact on consumer price index. Motunrayo, O.A., Nicholas, M.O. (2020), Asymmetric effect of oil price Advances and Applications in Statistics, 52(1), 33-54. on economic growth: Panel analysis of low-income oil-importing Dinh, D.V. (2019a), Analysed money supply and inflation: Evidence countries. Energy Reports, 6(2020), 1057-1066. from vietnam economy. Advances and Applications in Statistics, Muhammad, S., Suleman, S., Wei, C., Muhammad, N.M. (2017), 56(2), 125-142. Dynamics of electricity consumption, oil price and economic growth: Dinh, D.V. (2019b), Applied individual investment risk measurement Global perspective. Energy Policy, 108(2017), 256-270. method to forecast expected return rate. International Journal of Niaz, B.B., Josộ, R.M. (2013), How crude oil consumption impacts on Applied Mathematics and Statistics, 58(3), 60-72. economic growth of Sub-Saharan Africa? Energy, 54(1), 74-83. Dinh, D.V. (2020a), Optimal inflation threshold and economic growth: Rao, B.B., editor. (2007), Cointegration for the Applied Economist. Ordinal regression model analysis. The Journal of Asian Finance, London, United Kingdom: Palgrave Macmillan UK. Economics and Business, 7(5), 91-102. Rebeca, J.R., Marcelo, S. (2006), Oil price shocks and real GDP growth: Dinh, D.V. (2020b), Determinants of commercial banks’ profitability: Empirical evidence for some OECD countries. Applied Economics, Evidence from vietnam. Advances and Applications in Statistics, 37(2), 201-228. 63(1), 39-58. Riadh, B., Mohamed, A., Hatem, J. (2017), Renewable energy Dinh, D.V. (2020c), Forecasting domestic credit growth based on ARIMA consumption, International trade, oil price and economic growth model: Evidence from Vietnam and China. Management Science inter-linkages: The case of Tunisia. Renewable and Sustainable Letters, 10(5), 1001-1010. Energy Reviews, 76(2017), 620-627. Dinh, D.V. (2020d), Impulse response of inflation to economic growth Sandrine, L., Valộrie, M. (2008), Oil prices and economic activity: An dynamics: VAR model analysis. The Journal of Asian Finance, asymmetric cointegration approach. Energy Economics, 30(3), Economics and Business, 7(9), 219-228. 847-855. Doan Van, D. (2019c), Money supply and inflation impact on economic Shuddhasawtta, R., Ruhul, S., Harry, B. (2009), Impact of crude oil price growth. Journal of Financial Economic Policy, 12(1), 121-136. volatility on economic activities: An empirical investigation in the Federal Reserve Economic Data. (2020), Federal Reserve Economic Data. Thai economy. Resources Policy, 34(3), 121-132. Available from: The World Bank Group. (2020), Washington, DC, United States: The Jungwook, P., Ronald, A. (2008), Oil price shocks and stock markets in World Bank Group. Available from: the U.S. and 13 European countries. Energy Economics, 30(2008), org/indicator/ny.gdp.mktp.kd.zg. 2587-2608. Uchechukwu, J., Kamiar, M., Hamid, M. (2019), Oil price volatility, Lỹtkepohl, H. (2007), New Introduction to Multiple Time Series Analysis. financial institutions and economic growth. Energy Policy, Berlin: Springer. 126(2019), 131-144. Mehmet, B., Reneộ, V.E., Josine, U., Rangan, G. (2017), The impact Zied, F., Khaled, G., Frộdộric, T., Slim, C. (2016), Relationship between of oil price on South African GDP growth: A Bayesian markov crude oil prices and economic growth in selected OPEC countries. switching-VAR analysis. African Development Review, 29(2), The Journal of Applied Business Research, 32(1), 11-22. 120 International Journal of Energy Economics and Policy | Vol 11 • Issue 3 • 2021