Relationship Between Crude Oil prices and Macro-economic Variables: Evidence from BRICS Countries

The article analyses the relationship between Crude oil Prices and Macro-economic variables in BRICS countries using Quarterly data from March 31, 1999 to December 31, 2019 and an Autoregressive Distributed lag model has been developed to study the long term relationship between Crude oil and Macro-economic variable. The study found out that the long term relationship exists between the variables. We have also identified that all the countries react differently to the fluctuations in Oil prices. But interestingly China and India share some commonalities in terms of reacting to the changes in Crude Oil prices. Additionaly we have also found that fluctuations in the Oil price effect Trade Openness in every country under study except Russia.


INTRODUCTION
The rise in the interdependence of global financial markets has accelerated the growth and sensitivity to commodity prices (Tang and Xiong, 2012). Oil considered the primary source of energy for the world. Currently, there are more than 100 Oil-exporting countries in the world, whereas Oil prices affect both the participant's Oil importers and Oil exporter. In the latest scenario, it has been noticed that the shoot up of global commodity prices may bring various challenges to most of the countries. Goldman Sachs coined the term BRIC in Global economic paper 2001, titled "Building Better Global economies BRIC." Instringlely in December 2010, South Africa joined the former group and formed BRICS. As per World Bank; The BRICS countries account for 25% of the world GDP, nearly 50% of the global population, and around 20% of global merchandise trade. The economic size of these countries also increased the share in world energy consumption. As per BP statistical review, 2017; The energy consumption rate of BRICS consuming 36% of the total primary energy has increased by 16% in the last decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016).
In order to sustain high growth In the absolute sense, Oil consumption grew up by an average of 1.4 million barrels/day (mb/d). The developing world dominates this growth with China (0.7 mb/d), India (0.3 mb/d), and US (0.5 mb/d), accounting for almost two-thirds of the global increase (BP, 2019) Whereas Chinas contributes 4.5% to global renewables which is more the entire OECD countries combined. In BRICS, Crude Oil prices play a significant role in policymaking, since the fluctuations in crude Oil may harm the economy in various ways. Firstly, the effect can lead to a high cost of production with increased Inflation. Secondly, In markets, the investors and consumers confidence and level of growth of the economy may come down drastically. Also, the crude Oil importing countries will have to face various challenges compare to exporting countries. Figure 1 indicates that there is a constant increase in Crude Oil consumption from 2008 to 2018 in BRICS countries. In Brazil, the consumption of Oil was 2481 mb/d in 2008 has increased with a CAGR of 2.19% every year till 2018. Whereas China leads with a CAGR of 5.51% and India with 5.09% of consumption increase every year. Similarly, in South Africa, there is a small percentage increase that is 0.42% CAGR every year; interestingly, we can see a negative shift in the consumption of Oil in Russia.

LITERATURE REVIEW
Several studies have examined the relationship between Crude Oil prices and Macro-economic variables of selected countries and various groups of countries. While very few have investigated the relationship between selected Macro-economic variables and Crude Oil.
In this section, we elaborate on the literature review on Crude Oil and Macro-economic determinants across various economies. (Basnet and Upadhyaya, 2015) analyzed the impact of Crude Oil price shocks on Inflation, real output, and an Exchange rate of ASEAN-5 countries using the Structural VAR approach (SVAR). Where they have stated that the Macro-economic Variables are cointegrated and share the long term trend. They have also asserted that Oil price shocks do not explain the significant variation in Macro-economic variables.

Studies Outside BRICS Countries
Similarly; (Bhat et al., 2018) concluded that there exists a long term relationship between Crude Oil and Macro-economic Variables under study. Interestingly they pointed out the dominance of external shock in influencing domestic variables after their own Oil price shocks. (Zahran, 2019) Examined that Oil price shocks are significantly impacting Macro-economic variables in the short and medium-term but insignificant in the long run. Whereas (Arfaoui and Rejeb, 2017) found a negative relationship between Oil price and Macro-economic variables such as Exchange Rate and Gold prices. Identically; (Omolade et al., 2019) Investigated the influence of Crude Oil price shocks on the Macro-economic Variables with a conclusion that structural Inflation impacts more to Oil price than monetary Inflation. Similar results we have found out with (Koh, 2016), (Salami and Haron, 2018), (Ratti and Vespignani, 2016), (Aggarwal and Manish, 2020), (Malik et al., 2017) where they conformed the relationship between Crude Oil prices and Macro-economic Variables.

Studies Related to BRICS Countries
Similarly, few studies tries to examine the relationship between Macro-economic Variables and Crude Oil prices in BRICS countries. These studies show similar results but mixed conclusions; these are (Yildirim and Yildirim, 2019) examined and concluded that Crude Oil prices and Economic growth are having bidirectional causality whereas (Singh Tomar and Singh, 2016), concluded that there is no clear direction of causality between the Variables. Indistinguishably, (Sreenu, 2019), (Gupta and Sharma, 2018). (Mensi et al., 2017) (Raza, Shahzad, Tiwari, and Shahbaz, 2016) shows similar results. (Negi, 2015) concluded that China and India share a negative relationship with Crude Oil and Gross domestic Product whereas; Russia and Brazil have a positive relationship between the variables. So, the literature has helped us in choosing the Macro-economic variables that may gauge the Crude Oil in BRICS countries.

Data Description and Sources
The data set consists of quarterly observations from March 31, 1999 to December 31, 2019 for Brazil, Russia, India, China, and South Africa as a five developing and Emerging economies of the world. The data set of BRICS countries has been obtained from Bloomberg, Fred Reserve database, OECD (The Organisation for Economic Co-operation and Development database), World Bank, and Central and Reserve bank of respective countries. Based on the available literature as a set of potential variables, which includes Industrial Production (IP), Trade Openness (TO), Gross Domestic Product (GDP), Foreign Direct Investment (FDI), Exchange Rate(ER), Money Supply (MS), and Inflation. We have used M3 as a proxy of Money Supply, Consumer Price Index (CPI) as a proxy of Inflation and Trade Openness we have calculated with the help of Import, Export, and GDP and as a dependent variable, we have used WestTexes Intermediate (WTI) as a proxy of Crude Oil (As specified in Table 1). For the purpose of estimation following model has been used:  (1). To estimate we have used the difference of log variable, i.e. in logarithmic form whereas e represents the error term in growth model as shown in Equation (2) and Equation (3).
Where Δ represents changes in CRUDE and significant Macroeconomic variables, and et-1 represents for error correction term (ECT).The coefficient sign explains the speed of adjustment to CRUDE towards the long term path and it is expected to be negative Katircioglu, (2010).

TECHNIQUES AND METHODS
To find out the relationship between the Crude Oil and Selected Macro-economic Variables of BRICS countries. We have used the Autoregressive Distributed Lag (ARDL) Cointegration technique or Bound Cointegration technique. But Firstly, as this data is time series, we must undergo the stationary properties of the data.

Unit Root Test
Most of the techniques applied in modelling the time series data are majorly concerned with Stationary properties of the data. If a time series has a unit root than series is considered as a non-stationary, while the absence of it entails stationarity. The non-stationary series can result in spurious regression. The statistical procedure applied to determine the stationarity of the time series is called "Unit root test." The present study uses the Augmented Dickey-Fuller (ADF) test to examine the properties of time series data and make them stationary.

Augmented Dickey-Fuller (ADF) test
It is the most common method of unit root test. Suppose consider the series "Y" for testing unit root. With this series, the following ADF model can be developed as in Equation (4): is the nu; hypothesis of ADF test and alternative is δ < 0. If we do not reject the null hypothesis, then the series is said to be non-stationary and vice versa.

ARDL Cointegration Technique or Bound Cointegration technique
We cannot directly apply Johanson Cointegration test if selected variable under study are of mixed order of integration, or each variable is stationary but not in I(1). As in the case, we have to select ARDL modelling with the ordinary least square (OLS) model, which applies to both non-stationary and with mixed order of integration. From ARDL, with the help of simple linear trasformation Dynamic error correction (ECM) model can be derived. Wharas ECM integrates short-run dynamics with long-run equilibrium without losing long-run information and also helps to avoid the problem of spurious relationship.
The Model of ARDL as follows, as shown in Equation (5): The error correction version of the ARDL model shown in Equation (6): In the Equation (5) with β,δ and e represent short-run dynamics, and in Equation (6) ʎ s exhibits long-run relationship. The null hypothesis is ʎ 1 + ʎ 2 + ʎ 3 =0, symbolizes non-existence of long term relationship.  A structure of Unrestricted error correction model has been developed after determining the ARDL approach. As indicated by the Unit root test, all variables are stationary and integrated at I(0) or I(1). So now it is possible to study the long-run K signifies the number of regressors in the ARDL model for the dependent variable, F 0 , F 1, and F 2 represents the F-statistic of the Model with unrestricted intercept and restricted trend, unrestricted Intercept and trend, and unrestricted intercept and no trend respectively. Source: (Narayan, 2005) for F-statistics.

EMPIRICAL RESULTS
relationship between the variables using Bound test with the help of the regressors in Equation (2). The critical values of F-test using small sample are taken from (Narayan, 2005) and presented in Table 3.  To select a number of lags required for the cointegration test Schwartz Criteria (SC) was used. F 0 , F 1 and F 2 represent the F-statistic of the Model with unrestricted intercept and restricted trend, unrestricted Intercept and trend, and unrestricted intercept and no trend respectively; "a," "b," "c" indicates that the statistic lies below the lower bound, falls within the lower and upper bounds and lies above the upper bound respectively.   3,3,3,4,1,3,3,3) Russia ARDL (3,3,0,0,2,1,0,4) India ARDL (1,1,0,4,0,0,2,2) China ARDL (2,4,1,3,2,0,3,2) South Africa ARDL (1,1,1,1,0,0,1,1 From Table 4. We have already concluded the long term relationship between the variables. For further analysis, we have to check the stability and reliability of the model with serial correlation and CUSUM plot before estimating the long run and short-run coefficient. For serial correlation, we have used Breusch-Godfrey Serial LM Correlation Test for each model with the null hypothesis of no serial correlation or autocorrelation between the variables because F-statistics is more than 10 per cent, 5 per cent and 1 per cent level of significance as stated in Table 5. Whereas for analyzing the stability of the model. We have used the CUSUM test for ARDL models under study. The given plot in Figure 2 concludes that the models are stabled and can be used Source: Author's compilation for further investigation because CUSUM statistics lies between 5% critical bound. Table 6 estimates the level coefficient in the long run through the ARDL approach. In Brazil, the long term coefficient of Trade Openness, Industrial production and FDI is 0.88, 1.81, and 0.02 respectively significant at 1% and per cent level of significance. Whareas in the case of Russia, Exchange rate and Industrial Production, i.e. −0.71 and 1.37 is significant at 1% and 5% respectively. Although, India and China show a similar situation with Trade Openness and Money Supply, i.e. 0.80, 0.96 and −1.06, 0.12 respectively significant at 1% and 5% level. Additionaly, in India, GDP is significant at 5%, and in China, Inflation is at 10 per cent level. Whareas in South Africa, we can observe that only Trade Openness is 0.00, which is significant at the 5% level.  Whereas the remaining countries, the highest ECT has been obtained from India (-0.99), Brazil (-0.83) and Russia (-0.61) respectively. Which are statistically significant at (P<0.01). Additionaly, Table 7 and Table 7(a) shows the short-run dynamics of the ARDL process. Similarly, all independent and dependent variable shows a mixed reaction (either Positive or Negative) with each other.

CONCLUSION
The connection between Crude Oil and Macro-economic variables is relevant to BRICS countries because it is quite vulnerable to Oil prices shocks and interdependencies among the variable will put forward underlying importance for Managerial decisions of Investment and policymakers, Government and Investors as a whole. The aim of this paper is to highlight the relationship between Crude Oil and Macro-economic Variables of an Emerging and developing BRICS countries using Autoregressive Distributed lag, and Bound test approach with the quarterly observations for the period March 31, 1999 to December 31, 2019.  1, 1, 0, 4, 0, 0, 2, 2) 1, 1, 1, 1, 0, 0, 1, 1)