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\title{Relationship between Oil Prices and Stock Market Index: A Case of Pak, India \& China}
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             \author[1]{Waqar  Hassan}

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\date{\small \em Received: 11 December 2015 Accepted: 4 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


Cost of oil affects the profit and loss of the organization as oil is immediate or circuitous expense of operation. In this way, the ascent in crude oil costs will be relied upon to bring about the decline in income, which brought about a decrease of prompt stock market index. The goal of the examination is to get precise answers of exploration inquiries said in particular settings of Pakistan, India and China. This paper utilized regression, Durbin Watson test and correlation analysis to discover the answers of exploration inquiries and goals. The time of study is 21 years (From Jan 1995 to June 2015 on the bases of month to month variations) of dependent and predictor variables. It could be seen that the model is superbly fitted to the regression. In all instances of these three stock trades there is sure relationship between oil costs and stock exchange 100 index.

\end{abstract}


\keywords{oil prices, emerging market, stock market index, stock market return.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
tock market plays a vital role in surveying any nation's financial circumstance through enhanced stock return appeared by the higher interests of the organization as a consequence of monetary development. Stock market is a standout amongst the most pertinent and most essential measurements for administration and shareholders of an association. In view of the scholars and the candidates need to improve the administration procedure that might want to offer to balance out the execution components of examination that influence stock market index return rushing to ensured and predominantly look into database \hyperref[b16]{(Malik, 2007)}.\par
High oil costs are managing serious macroeconomic modification nation is running an extensive deficiency of outside trade saves. The objective in this paper is to reveal insight into the way of the effect of the oil stun in the full scale monetary circumstance for Pakistan. You can utilize the open economy to dissect the effect of crude oil cost in Pakistan on yield development. In this paper, the State Bank is seeking after the expansion target actualizing the money related approach so as to keep up the development of yield and value dependability working alongside the capacity of fiscal strategy on the estimation \hyperref[b18]{(Sharif E., 2005)}.\par
Worldwide oil costs subsequent to 2003 shows about stable ascent, as it was twofold in April 2006 than cost in January 2004 \hyperref[b5]{(Becon, 2005)}. Request, supply, theoretical considers and rise their interrelationships crude oil value all prompts a consistent ascent in oil costs.\par
In the most recent couple of years, worldwide interest for oil has developed in light of the solid financial execution in the improvement in Asia particularly China and India. World interest has developed at a rate of 1.3\%, while (blend) for People Republic China and India at a rate of 7\% in 2003 from 1990, and around 40\% of development popular \hyperref[b12]{(Hamilton J., 2008)}.\par
Another component that adds to the solid interest is a low level of stocks and their reproduction in industrialized nations in the instability of the time of supply. What's more, a portion of the Asian nations have started to construct their very own store \hyperref[b15]{(Koranchelian, 2005)}.\par
Likewise, this area will express a few perspectives on the likelihood of high oil costs impact on large scale economy level of Pakistan. Progressed of any inconsistencies in the supply and relies on upon the cost of the oil creating nations, huge numbers of the case this have an expansive influence of economy and imports crude oil keeping in mind the end goal to meet the powerless business. After a sudden surge in crude oil costs subsequent to 2003, as a special case creating nations depend vigorously on oil imports confronting the risk of expanded unsteadiness of the full scale economy \hyperref[b2]{(Arif \& Khalid, 2015)}.\par
This short note with world's crude oil costs saw the value patterns of local heater and rapid diesel oil as an outline since perception of 2002, and that need to concentrate on heater oil and fast diesel oil value patterns constitute the aggregate creation, breaking down the commitment to the generation, heater oil 29.4\% and diesel 31\% with top offer emerging from the reality in Pakistan \hyperref[b11]{(Hamilton \& Herrera, 2002}).\par
Pakistan's securities exchange in spite of its little size is a developing stock exchange, with the potential significance for financial specialists of the world. In any case, Pakistan relies on upon oil imports keeping in mind the end goal to run the monetary machine as an aftereffect of crude oil value stun, which may have the impact of destabilizing the residential money related markets. An adjustment in the cost of crude oil implies that you influence the instability of money markets return \hyperref[b1]{(Amir, 2008)}.\par
This study considers the non-straight relationship between crude oil costs and yield. In the event that the non-direct relationship exists than what is limit level after it gets to be negative. As indicated by the arrangement of this paper, first (I) portion explains about introduction, second (II) about literature review, area III will depict the methodology; Section IV clarifies the experimental discoveries. At long last, Section V will finish up the study with results and recommendations. 
\section[{a) Objectives of the research}]{a) Objectives of the research}\par
The objective of the research is to get accurate answers of research questions mentioned above in specific contexts of Pakistan. Is oil prices and stock price related? This is the first question that will confirm the relationship; there may be positive or negative relationship, if relationship exists than it will be yes to answer the question. The second objective of the research is to find out the answer of this question if oil prices declines what will be the impact on stock price? There may be decline in oil prices and it may increase the stock prices, its one possibility on the other hand there may be decrease in stock price with decrease in oil prices. 
\section[{b) Gap statement}]{b) Gap statement}\par
In different countries of the world, a ton of exploration has been finished with the study of stock market index. Considering this examination there is no work done in this term of office of day and age by any scientist on Pakistan, India and China (from 1995 to 2015), when contrasted with the crude oil cost. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
This segment clarifies the strategies and systems utilized by various specialists while considering the same theme or point moreover. This segment gives the exact thought regarding diverse sorts of techniques and results. This segment gives the thought regarding:  \hyperref[b0]{(Alou \& Amaze, 2009)}. They concentrated on two noteworthy crude oil markets like WTI and Brent, and three propelled securities exchange: France, the United Kingdom and Japan. The outcomes, expands the variable net oil costs have demonstrated that assumes an imperative part in deciding both the move probabilities between the genuine return unpredictability and administration \hyperref[b0]{(Alou \& Amaze, 2009)}. \hyperref[b19]{Sharif et al. (2005)} inspected the connection between the stock returns and oil costs of oil and gas part of the United Kingdom. They relationship between two variables was set up altogether positive \hyperref[b19]{(Sharif, Khan, \& Javed, 2005)}.\par
Ciner (2001) indicated proof of non-direct causal impact of crude oil cost in the universal securities exchange \hyperref[b9]{(Ciner, 2001)}.\par
Chang and Alder (2010) utilized the confirmation of most ward restrictive connection examination between the major money related markets that indicated crude oil market and the FTSE100, NYSE, S\&P 500 including the Dow Jones record \hyperref[b8]{(Chang \& Alder, 2010)}.\par
As far as the Petroleum, Exporting Countries \hyperref[b6]{Bjorn (2008)} analyzed the dynamic relationship between six GCC markets. An expansion of 10\% of the crude oil cost before the effect of oil costs cease to exist step by step shows that at first has made return of 2.5\% Norwegian stock. They investigated week after week time frame from 1997-2000, utilizing the VEC model, they discovered solid confirmation of association and between these business sectors \hyperref[b6]{(Bjorn, 2008)}.\par
Jacobsen and Matt (2008) tried whether the yearly change in the cost of crude oil would have the capacity to foresee the arrival of the worldwide securities exchange. Utilize the information from the eighteen created and thirty developing markets, they find huge consistency in 12 out of 18 created nations. Developing markets demonstrated same impact with lesser significance \hyperref[b14]{(Jacobsen \& Matt, 2008)}.\par
With a specific end goal to examine the response of stock market index of the GCC nations, Agoura Le Diana and Delilah (2010) led their study on oil value stun. This exploration utilized the direct and nonstraight models. Their discoveries demonstrated that securities exchange returns responded essentially to change the cost of crude oil in UAE, Qatar, Oman, Saudi Arabia and Bahrain. Change in crude oil cost for Kuwait found that it doesn't influence stock market index return \hyperref[b10]{(Diana \& Delilah, 2010)}.\par
Ravichandran and Alkhathlan (2010) utilizing day by day information amid the time of March 2008 to April 2010 upon crude oil cost and securities exchange to check that it impact the adjustments in the crude oil cost concerning stock market index cost in Gulf Cooperation Council for long haul returns (Ravichandran \& Alkhathlan, 2010). \hyperref[b13]{Iwayemi (2011)} utilizing the quarterly information of 1985-2007, which demonstrated that the stun of oil costs don't significantly affect the greater part of the macroeconomic variables like oil fares and effect of oil value stun in Nigeria. Granger-causality test, motivation reaction capacity and difference decay investigation the greater part of the outcomes demonstrated that the diverse measures of direct and positive oil stun yield, government spending, not the reason for expansion, and the genuine cost of raw petroleum. It has a vast negative oil stun, following found that cause the real crude oil costs and yield \hyperref[b13]{(Iwayemi, 2011)}. 
\section[{III.}]{III.} 
\section[{Research Methodology}]{Research Methodology}\par
In chapter 2 have discussed literature review from different articles to find out the results of our study regarding the relationship between crude oil prices and stock prices of Shanghai Stock Exchange 100 index (SSE-100), Bombay Stock Exchange 100 index (BSE-100) and Karachi Stock Exchange 100 index (KSE-100). This chapter will discuss the empirical finding and results of the study. 
\section[{a) Variables of research}]{a) Variables of research}\par
Two types of variables were used on these three countries like, India, China and Pakistan.  
\section[{d) Regression}]{d) Regression}\par
The general form of each type of regression is: Linear Regression: Y = a + bX + u Where: Y= the variable that we are trying to predict (Stock market Index) X= the variable that we are using to predict Y (Oil Prices) a= the intercept b= the slope u= the regression residual\par
In multiple regressions, the separate variables are differentiated by using subscripted numbers. Ho: there is NO relationship between dependent (Stock market index) and independent (Crude oil prices) variables H1: there is positive correlation between dependent (Stock market index) and independent (Crude oil prices) variables ? = 5\% Decision Criteria = Reject Ho, if P value is less than ?. Or "Accept" Ho, if P value is greater than ?. 
\section[{Oil price and stock market index from 1995 to 2015}]{Oil price and stock market index from 1995 to 2015} 
\section[{India}]{India}\par
The standard for analysis will depend on 95\% level of significance. In results of regression if a P values is less than ?. It means, if the correlation among the variables will be more than 95 than relationship will be accepted otherwise rejected.\par
IV. 
\section[{Analysis a) Methods for Analysis}]{Analysis a) Methods for Analysis}\par
This paper is about the relationship between crude oil prices and stock market index prices in China, India and Pakistan. To determine the relationship and interdependence of both types of variables, regression, Durbin Watson test and correlation analysis will tell about positive, negative, weak or strong relationship between the variables. Square and Adjusted R Square are 35.90\% and 35.60\%, it is not good to have the values of R square less than 60\% This also tells that how much output variable's variance is explained by the input variable's variance. The adjusted R square explains the accuracy of regression equation. In case of Pakistan, the value of R square is 0.078 means 7.8\% variation is explained. Our first indicator of generalizing is the adjusted R Square value (7.43\%). 
\section[{b) Correlation results}]{b) Correlation results} 
\section[{Correlation and descriptive}]{Correlation and descriptive}\par
In case of India (India): Here, the value of R square is 0.8855 means 88.55\% variation is explained. In general, the higher the R-squared, the better the model fits your data. Our first indicator of generalizing is the adjusted R Square value (88.50\%), which is adjusted for the number of variables included in the regression equation. This is used to estimate the expected shrinkage in R Square that would not generalize to the population because our solution is over-fitted to the data set by including independent variables.\par
After that F significance value which is approximately to zero (0.000) in all cases (Pakistan, India and China) tells that the results are not by chance, in other words there is zero probability of 'by chance' results. 
\section[{ii. Interpretation of P-Values in Linear Regression Analysis}]{ii. Interpretation of P-Values in Linear Regression Analysis}\par
The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). Here p-value is (0.000) approximately equal to zero, A low p-value (< 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor's value are related to changes in the response variable. In all three cases, P value is 0.00 which is lower to 5\%. It indicates that it is statistically significant as it is lower than 5\%. 
\section[{iii. Interpretation of Durbin-Watson}]{iii. Interpretation of Durbin-Watson}\par
According to Durbin Watson test, there correlation between variables in all three cases like China, India and Pakistan, the values of Durbin-Watson test are near to zero that represents that there is positive relationship between variables. 
\section[{iv. Summary of the results}]{iv. Summary of the results}\par
Summarizing the output by correlation, regression and Durbin Watson test analysis that the index points have positive correlation with crude oil prices in all three countries, taken as crude oil independent variable but the relationship is not too strong in the case of shanghai stock exchange have positive correlation only up to 0.5155. Crude oil prices have positive correlation with Indian stock exchange prices On the other hand; the case of Pakistani stock exchange is not different from Indian BSE, these both stock exchanges have positive correlation with oil prices. 
\section[{d) Hypothesis statement}]{d) Hypothesis statement}\par
H0 =there is no interdependence between crude oil prices in international market and stock market index H1 =there is interdependence between crude oil prices in international market and stock market index V. 
\section[{Conclusive Remarks}]{Conclusive Remarks}\par
In case of China and Pakistan and India, we can say that decreasing oil prices would also decrease the stock return in these countries. Analyzing the results, it could be seen that the model is perfectly fitted to the regression analysis. Studying the results one could say that the regression analysis is best for the analysis. In case of Chinese stock exchange, Indian stock exchange and Karachi stock exchange, Stock markets depends on the oil prices. It means these stock markets are dependent markets, we can say increasing the oil prices in the market will increase the price index of all three stock exchanges and decreasing the oil prices would decrease the stock market index. In other words we can say that oil prices transmit to the emerging stock market, in all three cases there is positive correlation among the variables defined as oil prices and stock market return All three statistical tools show same results as positive relationship between variables.\par
Shanghai Stock Exchange: reject null hypothesis saying that there is interdependence between crude oil prices in international market and SSE-100 index.\par
Bombay Stock Exchange: reject the null hypothesis and accept the alternate hypothesis saying that there is interdependence between crude oil prices in international market and BSE-100 index.\par
Karachi Stock Exchange: reject null hypothesis saying that there is interdependence between crude oil prices in international market and KSE-100 index. Shangai stock exchange and Karachi stock exchange depends on the oil prices; it is recommended that other factors could be found that relates to the stock market return. It is recommended that other stock exchange must be studied to know that results same or not? While it is clear that decreasing the oil prices does not increase the stock market return.  
\section[{Model}]{Model}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.02944642857142857\textwidth}P{0.7428392857142857\textwidth}P{0.026410714285714287\textwidth}P{0.013053571428571428\textwidth}P{0.011535714285714285\textwidth}P{0.026714285714285715\textwidth}}
\tabcellsep \multicolumn{4}{l}{Relationship between Oil Prices and Stock Market Index: A Case of Pak, India \& China}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Correlations}\\
\tabcellsep \tabcellsep \tabcellsep BSE100\tabcellsep OilPrices\\
\tabcellsep Pearson\tabcellsep BSE100\tabcellsep 1.000\tabcellsep .941\\
\tabcellsep Correlation\tabcellsep Oil Prices\tabcellsep .941\tabcellsep 1.000\\
\tabcellsep Sig. (1-tailed)\tabcellsep BSE100\tabcellsep \tabcellsep .000\\
\tabcellsep \tabcellsep Oil Prices\tabcellsep .000\\
\tabcellsep N\tabcellsep BSE100\tabcellsep 235\tabcellsep 235\\
\tabcellsep \tabcellsep Oil Prices\tabcellsep 235\tabcellsep 235\\
2016\tabcellsep \tabcellsep \multicolumn{2}{l}{Variables Entered/Removed a}\tabcellsep 2016\\
Year 26 Volume XVI Issue VI Version I ( ) B\tabcellsep \multicolumn{4}{l}{Descriptive Statistics Mean Std. Deviation Variables Entered Variables Removed 1916.3954 925.45021 52.7596 34.57832 Correlations Oil Prices b SSE100 SSE100 1.000 Oil Prices .599 SSE100 Oil Prices .000 SSE100 235 Oil Prices 235 Analyzing this value we can say that there is Model 1 SSE100 Oil Prices Pearson Correlation Sig. (1-tailed) N positive relationship to the extent of strong positive, Depending on this value one can conclude that there is relationship exists. As a result we can say that increase Method N 235 235 Enter OilPrices .599 1.000 .000 235 235 in oil prices increases the stock exchange index of BSE-100. Correlation and descriptive statistics on KSE-100 and crude oil prices Descriptive Statistics Mean Std. Deviation N KSE100 7439.8123 6774.67537 235 Oil Price 80.2644 426.60696 235 a. Dependent Variable: BSE100 b. All requested variables entered.}\tabcellsep Year ( ) Volume XVI Issue VI Version I\\
Global Journal of Management and Business Research\tabcellsep \multicolumn{4}{l}{Variables Entered/Removed a Variables Entered Variables Removed Oil Prices b Analyzing the correlation results, it seem that Model 1 there is positive correlation among all the variables, first there is observation upon the oil prices in international market and the stock prices in Shanghai Stock Exchange, keep in mind that the stock prices are taken SSE-100. There is positive correlation among the Method Enter variable, but the results shows positive correlation, depending upon this resultant figure of 0.59 between crude oil prices and Shanghai Stock Exchange's 100-index. Correlation and descriptive statistics on BSE-100 and crude oil prices Descriptive Statistics Mean Std. Deviation N a. Dependent Variable: SSE100 b. All requested variables entered. Correlations Variables Entered/Removed a Model Variables Entered Variables Removed Method 1 Oil Price b Enter KSE100 Oil Price Pearson Correlation KSE100 1.000 .279 Oil Price .279 1.000 Sig. (1-tailed) KSE100 .000 Oil Price .000 N KSE100 235 235 Oil Price 235 235 a. Dependent Variable: KSE100 b. All requested variables entered.}\tabcellsep Global Journal of Management and Business Research\\
\tabcellsep \multicolumn{2}{l}{BSE100 Third and last, there is observation upon the oil 2871.8253}\tabcellsep 2068.76452\tabcellsep 235\\
\tabcellsep \multicolumn{2}{l}{Oil Prices prices in international market, and the stock prices in 52.7596}\tabcellsep 34.57832\tabcellsep 235\\
\tabcellsep \multicolumn{2}{l}{Karachi Stock Exchange. There is positive correlation}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{among the variable, depending upon this resultant}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{figure of approximately 0.27 between crude oil prices}\tabcellsep \\
\tabcellsep and Karachi Stock Exchange's 100 index.\tabcellsep \tabcellsep \\
\tabcellsep © 2016 Global Journals Inc. (US) 1\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: c) Results for regression i. R-squared \& Adjusted R SquareIn case of China: If the adjusted R]} 
\caption{\label{tab_1}B}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.6056492027334852\textwidth}P{0.015876993166287014\textwidth}P{0.0023234624145785877\textwidth}P{0.048018223234624145\textwidth}P{0.012004555808656036\textwidth}P{0.022072892938496583\textwidth}P{0.02439635535307517\textwidth}P{0.011617312072892938\textwidth}P{0.022072892938496583\textwidth}P{0.0019362186788154897\textwidth}P{0.0011617312072892939\textwidth}P{0.03562642369020501\textwidth}P{0.04724373576309795\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients a}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Unstandardized}\tabcellsep \multicolumn{2}{l}{Standardized}\tabcellsep \tabcellsep \tabcellsep \tabcellsep 95.0\% Confidence\\
\tabcellsep \multicolumn{2}{l}{Model}\tabcellsep \multicolumn{3}{l}{Coefficients B Std. Error}\tabcellsep \multicolumn{3}{l}{Change Statistics Coefficients T Beta}\tabcellsep \multicolumn{2}{l}{Sig.}\tabcellsep Interval for B Lower Upper Bound Bound\\
1\tabcellsep \multicolumn{2}{l}{R (Constant) OilPrices}\tabcellsep \multicolumn{4}{l}{R Square -98.129 Adjusted R Square 83.689 Std. Error of the Estimate 56.292 1.328}\tabcellsep R Square Change .941\tabcellsep \multicolumn{3}{l}{F -1.173 Change df1 df2 .242 42.404 .000}\tabcellsep -263.012 Sig. F Change 53.677\tabcellsep Durbin-Watson 66.754 58.908\\
\tabcellsep 1\tabcellsep .599 a\tabcellsep .359\tabcellsep .356\tabcellsep \multicolumn{2}{l}{742.55373}\tabcellsep .359\tabcellsep 130.468\tabcellsep 1\tabcellsep 233\tabcellsep .000\tabcellsep .080\\
\multicolumn{5}{l}{a. Predictors: (Constant), Oil Prices b. Dependent Variable: SSE100}\tabcellsep \multicolumn{4}{l}{Regression analysis for Pakistan Model Summary b}\tabcellsep \tabcellsep \tabcellsep 2016\\
\multicolumn{12}{l}{ANOVA a Coefficients a Standardized Coefficients Beta Sum of Squares Unstandardized Coefficients B Std. Error Df 71938246.127 1 128472946.786 233 200411192.913 234 a. Dependent Variable: SSE100 Model Model 1 Regression Residual Total b. Predictors: (Constant), OilPrices Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change 1 .279 a .780 .743 6519.03564 .078 ANOVA a 1 (Constant) 1070.397 88.499 Oil Prices 16.035 1.404 .599 Regression analysis for India T Mean Square Sig. 71938246.127 551386.038 Change Statistics F Change df1 df2 95.0\% Confidence Interval for B Lower Bound Upper F Sig. 130.468 .000 b Durbin-Watson Sig. F Change 19.712 1 233 .000 .092 Bound 12.095 .000 896.036 1244.757 11.422 .000 13.269 18.801 Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin-Watson R Square Change F Change df1 df2 Sig. F Change 1 .941 a .885 .885 702.19205 .885 1798.076 1 233 .000 .220 ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 886583903.541 1 886583903.541 1798.076 .000 b a. Predictors: (Constant), OilPrices b. Dependent Variable: BSE100 a. Dependent Variable: SSE100 Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0\% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 7083.823 432.748 16.369 .000 6231.224 7936.422 Oil Price 4.435 .999 .279 4.440 .000 2.467 6.403}\tabcellsep Year Volume XVI Issue VI Version I ( ) B Global Journal of Management and Business Research\\
\tabcellsep \multicolumn{2}{l}{Residual}\tabcellsep \tabcellsep \multicolumn{2}{l}{114886165.309}\tabcellsep \tabcellsep 233\tabcellsep \multicolumn{2}{l}{493073.671}\tabcellsep \\
\tabcellsep Total\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{1001470068.849}\tabcellsep \tabcellsep 234\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{a. Dependent Variable: BSE100}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{b. Predictors: (Constant), OilPrices}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
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