Macroeconomics Dynamics: Exploring the Impact of Stock Market Development on Economic Growth in Mali

Macroeconomics Dynamics: Exploring the Impact of Stock Market Development on Economic Growth in Mali | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Macroeconomics Dynamics: Exploring the Impact of Stock Market Development on Economic Growth in Mali Boubacar Amadou CISSE, Aminata TEME, Salimou KEITA, Yahya SÖNMEZ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5067177/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Economic growth is often influenced by various factors, with stock market development playing a crucial role. This study investigates the relationship between the stock market development, specifically the Bourse Régionale des Valeurs Mobilières (BRVM), and economic growth in Mali. The BRVM serves eight West African countries, including Mali, and its development may impact regional economies. Utilizing monthly data from January 2009 to December 2020, this research examines the effects of BRVM Market Capitalization, BRVM Composite Index, Capital Flow, and Inflation on Mali's Gross Domestic Product (GDP). The study employs an Autoregressive Distributed Lag (ARDL) model and Vector Error Correction (VEC) model to analyze both short-run and long-run relationships. Findings suggest a significant long-run relationship between stock market development and economic growth in Mali, with inflation and capital flow having negative impacts, while market capitalization and the BRVM Composite Index show positive but less significant effects. The study contributes to understanding the macroeconomic dynamics within West African economies and the importance of stock market development in promoting economic growth. Gel: E02, E20, O10, F43 Macroeconomics Dynamics Economic Growth Stock Market Development 1. Introduction Economic growth has been a central topic in economic discussions for decades. It is generally defined as an increase in the production of goods and services within an economy, typically measured by Gross Domestic Product (GDP). Numerous factors influence economic growth, with stock markets being a significant indicator of a country’s economic health. Valderrama (2023) found that financial development contributes to economic growth, but the precise mechanisms and policies needed to promote growth remain inconclusive. Benhabib and Spiegel ( 2000 ) established that financial development positively impacts growth and investment, with indicators correlated to both total factor productivity growth and investment, but their correlations differ. In African economies, stock markets are still evolving and often require increased efficiency to attract more investors and companies. Despite their size, their impact on African economies cannot be overlooked, and the Regional Securities Exchange (Bourse Régionale des Valeurs Mobilières - BRVM) is a notable example. The BRVM serves as a regional exchange for eight West African countries, including the Republic of Mali. According to the World Bank (2024), Mali has a GDP growth rate of 3.5%, ranking 121st globally in terms of GDP (current US $ ) and 24th out of 47 Sub-Saharan African countries. Despite being considered "mostly unfree" with an economic freedom score below the global average but above the regional average (Index of Economic Freedom, 2024), Mali’s economy has shown resilience, maintaining slight growth amidst political and security challenges over the past decade. Mali, along with Cote d'Ivoire, Senegal, Burkina Faso, Niger, Togo, Benin, and Guinea-Bissau, shares the BRVM. The exchange is dominated by companies from Cote d'Ivoire and Senegal, the region's strongest economies. Many economic sectors are listed but health and technology companies are not. Currently, Mali has only one company listed in the BRVM's main compartment, the Bank of Africa Mali. Although this is a small presence among the 46 listed companies, various economic policies within the West African States Economic Community (ECOWAS) continue to promote economic integration and development (Sustainable Security Exchanges, 2023). Therefore, a regional economic integration is underway even though it still needs rooms for improvements. The question of “Does the regional stock exchange development affect Mali’s economy?” arises. This study investigates the existence of relationship between Mali’s economy growth and the BRVM development. In this study, research and publication ethics were complied with. 2. Literature Review Studies on economic growth and stock market development are not scarce among financial economics research topics. While most of them were conducted on developed economies markets, some are of international dimensions, and others are based on developing economies. Market development effects on asset growth is more noticeable in advance economies (Titman, Wei and Xie, 2013 ). However, this effect is not related to trading expenses and corporate governance (Titman, Wei & Xie, 2013 ). Other studies on the effect of stock market and banks on economic growth show that the former has more significant effect on economic growth than the latter (Arestis et al., 2001 ). It is worth reminding that banks are also strong players in the stock markets. Çiftçi et al (2017) found similar results but stressed that credit market contribute more on the positive of stock market development on economic growth. Following Schumpeter’s assertion (1911) that financial intermediaries’ core activities promote long term economic growth, Drebentsov, Bergsman and Broadman ( 1993 ) established there is strong correlation between financial development and economic growth, investment rates and capital efficiency. Most studies on developed nations have determined a positive relationship between market development and growth; this result was confirmed by Pradhan ( 2018 ) from his research on G-20 countries. He further stressed the need for sustainable economic policies. Oppositive results were found in Belgium by Tsaurai ( 2016 ) when he established the absence of long-term causality between stock market development and economic growth. However, Baral ( 2019 ) found a result in line with previous empirical studies in Nepal. Conclusive studies have also been conducted on emerging economies. El-Wassal (2008) carried out a study on 40 emerging economies from 1980 to the year 2000. He found that economic growth, financial freedom policies and foreign portfolio investment are the primary causes of stock market growth. Similarly, studies are scarce in Sub-Saharan African countries; Enisan and Olufisayo ( 2009 ) analyzed the impact of market development to economic growth. Using a bound test and Vector Error Correlation Model, they found that stock market development cause economic growth in Egypt and South African. Their study further established that there is a two-directional Granger causal relationship between stock market development and economic growth in Cote d’Ivoire, Kenya, Morocco and Zimbabwe. Furthermore, Adjasi and Biekpe ( 2006 ) conducted a similar study on 14 African nations. They found a positive impact on stock market development on economic growth in most upper-middle-income economies and in moderately capitalized markets. The study leads to the conclusion that more developed and capitalized economies in Africa are in better position to drive countries to economic growth. Alfonso and Reimers (2021) investigated on the relationship between stock markets and economic growth in African countries; they found that the introduction of stock market leads to a hump-shaped increased in GDP per capita; the effect peaks in the fifth year and becomes insignificant afterward. They stressed that ongoing market development and supportive policies are necessary for sustainable results. Different results were found by Kagochi and Durmaz ( 2020 ). They conducted a study on the relationship between stock market development, human capital and economic growth on Sub-Saharan Africa. They found that human capital positively affects economic growth unlike stock market development. Similar research was carried out by Ali and Adahama (2023) on selected African economies with focus on market development, monetary policy and economic growth. They underscore that both market development and monetary policy have a positive effect on economic Algeria, Angola, Libya and Nigeria. Yu, Hassan and Sanchez ( 2012 ) found different causal relationship between stock market development and financial factors across different regions and income groups. Nazir, Nawaz and Gilani ( 2010 ), on their study on Pakistan stock markets, established that the size and market capitalization can strongly influence economic growth. Filer, Hanousek and Campos ( 2000 ) had found similar results but stressed that the effect can lead to currency appreciation. In his comparative study between bank-based versus market-based financial systems, Tadesse ( 2002 ) observed that market-based systems are more effective in economies with developed financial sectors while bank-based systems have better performance in less developed financial environments. This indicates that countries’ conditions determine the financial infrastructure. In Mali, as in the rest of west Africa region, financial integration is enhanced by the West Africa Economic and Monetary Union (WAEMU) through the creation of a regional stock exchange: Bourse Regionale des Valeurs Mobilieres (El-Wassal, 2008). El-Wassal (2008) further stressed that financial freedom policies and foreign portfolio investment play a crucial role in promoting stock market growth; he also determined that reducing entry barriers and enhancing market efficiency attract foreign investments. The results of El Wassal’s study also indicated that low country risk promotes stock market growth. Unlike country risk, high interest rate negatively affects stock market development (Vazakidis and Adamopoulos, 2009 ). Levine and Zervos ( 1996 ) had indicated that, whilst there is a positive correlation between economic development and stock development, the correlation is but partial and not definitive. This may lead to the understanding that stock market development can be influenced by many factors; political stability, regulatory quality and the overall economic environment could all play important role. Studies on the correlation between stock market development and economic development have not always showed a positive correlation, in reference to Tsaurai ( 2016 ) and Filer et al. ( 2000 ). This leads to the conclusion that there is need for tailored policy approaches. Some argued that the effectiveness of financial systems is context-based and that policies meant for advocating the market and financial architecture must be adapted to each country’s specific conditions (Tadesse, 2002 ). 3. Data and Methodology In this section, data set, research methodology and empirical findings are presented. 3.1 Data Set This study looks possible relationships between economic growth and other macroeconomic factors, notably Inflation, BRVM Market Capitalization, Capital Flow and BRVM Composite Index. Gross Domestic Product (GDP) The economic well-being of a nation or region is frequently evaluated using Gross Domestic Product (GDP), which quantifies the total market worth of all final products and services generated and provided within a designated timeframe by one or multiple countries. It represents the economic growth in this study. Inflation As a widely used metric for gauging economic spending, the Consumer Price Index (CPI) serves as a valuable tool. In the context of this particular investigation, the CPI was employed to assess inflation levels. Market Capitalization The market capitalization of a publicly traded company represents the combined worth of all the common shares held by shareholders. This value is determined by multiplying the market price per common share by the total number of outstanding common shares. Capital Flows These include all transactions recorded in the capital accounts, as well as the financial account of the balance of payments, such as foreign direct investments (FDI), portfolio investments, and other investments including loans and banking capital. BRVM Composite Index The BRVM Composite is a stock index calculated from the value of each stock on the BRVM. The BRVM Composite is a West African stock market index created with the base 100 on September 15, 1998. Monthly data from January 2009 to December 2020 were used to conduct this study. Data were collected from International Monetary Fund, World Bank and BRVM websites. Not all data were available in monthly data; those available in daily and annual frequences, were converted into monthly frequencies. Table 1 Macroeconomic Factors Used in Analysis Variable Indicator Measurement Source Variable Type Economic Growth GDP \(\:\frac{{\text{G}\text{D}\text{P}}_{\text{t}}-\:{\text{G}\text{D}\text{P}}_{\text{t}-1}}{{\text{G}\text{D}\text{P}}_{\text{t}-1}}\) \(\:\text{L}\text{o}\text{g}(\frac{{\text{G}\text{D}\text{P}}_{\text{t}}}{{\text{G}\text{D}\text{P}}_{\text{t}-1}}\) ) World Bank Dependent Inflation Consumer Price Index \(\:\frac{\text{C}\text{P}\text{I}-\:{\text{C}\text{P}\text{I}}_{\text{t}-1}}{{\text{C}\text{P}\text{I}}_{\text{t}-1}}\) \(\:\text{L}\text{o}\text{g}\left(\frac{{\text{C}\text{P}\text{I}}_{\text{t}}}{{\text{C}\text{P}\text{I}}_{\text{t}-1}}\right)\) International Monetary Fund Independent Stock Market Index BRVM Composite \(\:\frac{{\text{P}}_{\text{t}}-\:{\text{P}}_{\text{t}-1}}{{\text{P}}_{\text{t}-1}}\) \(\:\text{L}\text{o}\text{g}\left(\frac{{\text{P}}_{\text{t}}}{{\text{P}}_{\text{t}-1}}\right)\) BRVM Independent Market Capitalization Market Capitalization \(\:\frac{{\text{M}\text{K}\text{C}}_{\text{t}}-\:{\text{M}\text{K}\text{C}}_{\text{t}-1}}{{\text{M}\text{K}\text{C}}_{\text{t}-1}}\) \(\:\text{L}\text{o}\text{g}\left(\frac{{\text{M}\text{K}\text{C}}_{\text{t}}}{{\text{M}\text{K}\text{C}}_{\text{t}-1}}\right)\) BRVM Independent Capital Flows Capital Flow \(\:\frac{{\text{C}\text{P}\text{F}}_{\text{t}}-\:{\text{C}\text{P}\text{F}}_{\text{t}-1}}{{\text{C}\text{P}\text{F}}_{\text{t}-1}}\) \(\:\text{L}\text{o}\text{g}\left(\frac{{\text{C}\text{P}\text{F}}_{\text{t}}}{{\text{C}\text{P}\text{F}}_{\text{t}-1}}\right)\) World Bank Independent Source : Generated by Author 3.2 Research Methodology This study explores the existence of relationship (long or short) between GDP and other variables (BRVM Composite, Market Capitalization, Capital flow and Inflation). Eviews 10 was used to perform the analyses. The generic research model determined is the following: \(\:{\text{G}\text{D}\text{P}}_{\text{i}\text{t}}={{\beta\:}}_{0}+\:{{\beta\:}}_{1}{\text{I}\text{N}\text{F}\text{L}}_{\text{i}\text{t}}+\:{{\beta\:}}_{2}{\text{M}\text{K}\text{C}}_{\text{i}\text{t}}+\:{{\beta\:}}_{3}{\text{C}\text{P}\text{F}}_{\text{i}\text{t}}+\:{{\beta\:}}_{4}{\text{B}\text{R}\text{V}\text{M}}_{\text{i}\text{t}}+\:{{\epsilon\:}}_{\text{i}\text{t}}\) (1) Where: β 0 Constant β 1 Inflation sensitivity monthly change β 2 Sensitivity of monthly change in Market Capitalization β 3 Sensitivity of monthly change in Capital Flow β 4 Sensitivity of monthly change in BRVM Composite Ꜫ it Error term The series normality test was first carried out. The variables logged values will be used to conduct the afterward-tests. The series stationarity test was then performed to detect a possible unit root; the test was run at both the Level and the First Difference. In the event where the series are I(0) and I(1), the Bound Test statistics and not the Johansen Test will be used for cointegration. The Bound Test is more appropriate because it is applicable on series at I(0) and I(1) and is more suitable for small and finite data samples. In the case of a Level relationship the variables, it will indicate that there are both short-run and long-run relationships between the variables. An Autoregressive Distributed Lag (ARDL) and a Vector Error Correction (VEC) models will be determined. The Error Correct model to be estimated is as follows: \(\:\varDelta\:{\text{G}\text{D}\text{P}}_{\text{T}}=\:{{\gamma\:}}_{0}+\:\sum\:_{\text{i}=0}^{\text{p}}{{\gamma\:}}_{1}{\varDelta\:\text{I}\text{N}\text{F}\text{L}}_{\text{t}-1}+\:\sum\:_{\text{i}=0}^{\text{P}}{{\phi\:}}_{\text{i}}{CPF}_{\text{t}-1}+\:\sum\:_{\text{i}=0}^{\text{P}}{{\phi\:}}_{\text{i}}{MKC}_{\text{t}-1}+\:\sum\:_{\text{i}=0}^{\text{P}}{{\phi\:}}_{\text{i}}{BRVM}_{\text{t}-1}\:+\:{{\mu\:}\text{E}\text{C}\text{T}}_{\text{t}-1}+\:{\text{u}}_{\text{i}}\) (2) γ 1 and φi stand for short-term coefficients, as for ∆ , it represents the difference operator, µ stands for the order of delay, u i represent the residuals and ECT t−1 signifies the term for error correction. The ECT is expressed as follows: $$\:{\text{G}\text{D}\text{P}}_{\text{t}-1}=\:{{\delta\:}}_{1}{\text{I}\text{N}\text{F}\text{L}}_{\text{t}-1}+\:{{\delta\:}}_{2}{\text{C}\text{P}\text{F}}_{\text{t}-1}+\:{{\delta\:}}_{3}{\text{M}\text{K}\text{C}}_{\text{t}-1}+\:{{\delta\:}}_{4}{\text{B}\text{R}\text{V}\text{M}}_{\text{t}-1}+\:{{\epsilon\:}}_{1}$$ 3 The long-term association between variables is displayed by ECT. The rate at which stock returns revert to equilibrium following a long-term divergence is measured by the u coefficient. The system is considered balanced if the error correction coefficient is less than 1, and when it is negatively indicated, it suggests that, in the event of a deviation from balance, there is a movement towards equilibrium. In other words, according to Bozkurt (2007: 166), the mistake correction process functions. 3.3 Empirical Findings The result for the normality test is as follows Table 2 Normality Test GDP CAPITAL_FLOW BRVM_COMP INFLATION_CHANGE MARKET_CAP Mean 3.889118 2.975233 -0.000436 -0.337083 0.085132 Median 4.754179 3.111801 -0.001400 -0.041667 0.106027 Maximum 7.084700 4.971522 0.152600 3.560000 0.353846 Minimum -1.235500 1.003936 -0.110900 -6.710000 -0.115385 Std. Dev. 2.225470 0.899308 0.044650 2.358703 0.101483 Skewness -0.721538 -0.190079 0.549780 -0.565118 -0.102936 Kurtosis 2.282130 2.454369 3.828300 2.692086 2.643682 Jarque-Bera 15.58682 2.653401 11.37067 8.233467 1.016077 Probability 0.000412 0.265351 0.003395 0.016298 0.601675 Sum 560.0330 428.4336 -0.062800 -48.54000 12.25895 Sum Sq. Dev. 708.2384 115.6519 0.285092 795.5778 1.472739 Observations 144 144 144 144 144 Source : Generated by Author The skewness’ different values are all close to zero (0), which is the normal value for skewness for a normally distributed series. The Kurtosis, that displays the peak of flatness of the distribution of a series, has the value 3 as the normal Kurtosis value for a normally distributed series; the different series in this study have kurtosis values close to 3. Theses give enough idea that the series are normally distributed. The Jarque-Bera statistics measure the difference the skewness the kurtosis of the variables. The null hypothesis states that the distribution is normal. At 1% significance level, it is safe to accept the null hypothesis. After this test, the logged values of the series were used to perform further tests. Next, the series stationarity test was performed, and the relative results are as follow: Table 3 Unit Root Test for Stationarity UNIT ROOT TEST RESULTS TABLE (ADF) Null Hypothesis: the variable has a unit root At Level LGDP LBRVM LCPF LINFL LMKC With Constant t-Statistic -2.4177 -10.9836 -2.3408 -3.1960 -1.3025 Prob. 0.1387 0.0000 0.1609 0.0223 0.6272 n0 *** n0 ** n0 With Constant & Trend t-Statistic -2.3991 -11.0415 -2.2295 -3.3079 -0.6541 Prob. 0.3785 0.0000 0.4690 0.0691 0.9737 n0 *** n0 * n0 Without Constant & Trend t-Statistic -0.7523 0.0300 -0.8969 -0.1454 -0.6330 Prob. 0.3889 0.6906 0.3259 0.6319 0.4413 n0 n0 n0 n0 n0 At First Difference d(LGDP) d(LBRVM) d(LCPF) d(LINFL) d(LMKC) With Constant t-Statistic -5.1972 -5.8691 -1.3978 -5.4405 -2.5570 Prob. 0.0000 0.0000 0.5816 0.0000 0.1047 *** *** n0 *** n0 With Constant & Trend t-Statistic -5.1808 -5.8046 -1.3210 -5.3427 -2.5787 Prob. 0.0002 0.0000 0.8783 0.0001 0.2908 *** *** n0 *** n0 Without Constant & Trend t-Statistic -5.2155 -5.8855 -1.3094 -5.4638 -2.5649 Prob. 0.0000 0.0000 0.1753 0.0000 0.0105 *** *** n0 *** ** Notes : a: (*)Significant at the 10%; (**)Significant at the 5%; (***) Significant at the 1% and (no) Not Significant b: Lag Length based on AIC c: Probability based on MacKinnon (1996) one-sided p-values. Source : Generated by Author The stationarity test depicts that some variables (BRVM and Inflation) are stationary at Level (I(0)) and the rests are I(1). It has been noticed that all the variables are stationary at first difference apart from Capital Flow. The result for the Bound Test for cointegration is as displayed below; Table 4 Bound Test for Cointegration F-Bounds Test Null Hypothesis: No levels relationship Test Statistic Value Signif. I(0) I(1) Asymptotic: n = 1000 F-statistic 2.993358 10% 2.2 3.09 k 4 5% 2.56 3.49 2.5% 2.88 3.87 1% 3.29 4.37 Actual Sample Size 142 Finite Sample: n = 80 10% 2.303 3.22 5% 2.688 3.698 1% 3.602 4.787 Source : Generated by Author The F-statistics value is superior to the level value (I(0)) value. This leads to the conclusion that there is enough significance (5%) to reject the null hypothesis of no long-run correlation between the variables. In other others, there is a long-run relationship between the variables. Both the long and short run models need to be estimated; appropriate techniques are the ARDL and VEC models. After running the ARDL models, it is necessary to determine the lag length for the afterward-analyses. Table 5 Lag selection Lag LogL LR FPE AIC SC HQ 0 -386.7358 NA 0.000219 5.760821 5.867904 5.804337 1 679.2595 2037.932 4.91e-11 -9.547933 -8.905436 -9.286839 2 990.2493 571.6725 7.33e-13* -13.75367* -12.57575* -13.27499* 3 1010.195 35.19851 7.93e-13 -13.67934 -11.96601 -12.98309 4 1018.648 14.29610 1.02e-12 -13.43601 -11.18727 -12.52218 5 1053.145 55.80269* 8.96e-13 -13.57566 -10.79150 -12.44425 6 1065.952 19.77595 1.09e-12 -13.39635 -10.07678 -12.04736 7 1076.532 15.55843 1.38e-12 -13.18429 -9.329306 -11.61772 8 1082.882 8.872365 1.87e-12 -12.91004 -8.519637 -11.12589 Source : Generated by Author As the most decision criteria (including Akaike Information Criteria - AIC), the optimal lag length is 2. The resulting ARDL model is determined below: Table 6 ARDL Model – Short Run Relationship Variable Coefficient Std. Error t-Statistic Prob.* LGDP(-1) 1.561969 0.069857 22.35951 0.0000 LGDP(-2) -0.579662 0.072438 -8.002134 0.0000 LMKC 0.358929 0.261641 1.371839 0.1725 LMKC(-1) -0.547928 0.262494 -2.087395 0.0388 LINFL -0.034949 0.015853 -2.204529 0.0292 LCPF -1.689745 0.545708 -3.096425 0.0024 LCPF(-1) 3.029527 1.041353 2.909222 0.0043 LCPF(-2) -1.403177 0.542557 -2.586228 0.0108 LBRVM 0.051809 0.135294 0.382938 0.7024 LBRVM(-1) -0.118675 0.133456 -0.889243 0.3755 LBRVM(-2) 0.274655 0.135520 2.026680 0.0447 C 0.091134 0.034009 2.679693 0.0083 R-squared 0.986939 Mean dependent var 0.743008 Adjusted R-squared 0.985834 S.D. dependent var 0.217211 S.E. of regression 0.025853 Akaike info criterion -4.392089 Sum squared resid 0.086886 Schwarz criterion -4.142301 Log likelihood 323.8383 Hannan-Quinn criter. -4.290585 F-statistic 893.0384 Durbin-Watson stat 2.066688 Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model selection. Source : Generated by Author The results show that 98.58% of change in GDP is explained by the regressors. Inflation and Capital Flow, with enough significance level, have both negative effects on GDP. The results also show that Market Capitalization and BRVM Composite index have positive influence on GDP; however, these variables do not have their coefficients significant at 10%. The long-run relationship results depict the following Table 7 VEC Model – Long Run Relationship Variable Coefficient Std. Error t-Statistic Prob. D(LGDP(-1)) 0.743704 0.069961 10.63024 0.0000 C -0.000246 0.002341 -0.105022 0.9165 D(LBRVM) 0.002949 0.118500 0.024884 0.9802 D(LBRVM(-1)) -0.232597 0.115459 -2.014544 0.0460 D(LCPF) -1.513056 0.593852 -2.547869 0.0120 D(LCPF(-1)) 1.412658 0.593474 2.380318 0.0187 D(LINFL) 0.048705 0.105546 0.461455 0.6452 D(LINFL(-1)) -0.073573 0.099911 -0.736380 0.4628 D(LMKC) 0.144769 0.261334 0.553963 0.5805 D(LMKC(-1)) -0.570278 0.323002 -1.765552 0.0798 ECT(-1) -0.022552 0.013302 -1.695332 0.0924 R-squared 0.538897 Mean dependent var -0.001108 Adjusted R-squared 0.503698 S.D. dependent var 0.037861 S.E. of regression 0.026673 Akaike info criterion -4.336057 Sum squared resid 0.093197 Schwarz criterion -4.107085 Log likelihood 318.8601 Hannan-Quinn criter. -4.243012 F-statistic 15.31015 Durbin-Watson stat 2.157282 Prob(F-statistic) 0.000000 Source : Generated by Author Following the results, it can be noticed that there is an ECT value of -0.0225. the negative sign indicates that shocks in the short run will be corrected in the long run at 10% significance level. In other words, the models will re-adjust itself in the long run after a shock in the long. To understand the nature of the relationship between the variables, the Granger Causality test was conducted, and the results are as depicted below. The null hypothesis for the test states that A does not Granger cause B. Table 8 Granger Causality Test Null Hypothesis: Obs F-Statistic Prob. Decision LBRVM does not Granger Cause LGDP 142 3.56159 0.0311 Reject LGDP does not Granger Cause LBRVM 0.32659 0.7219 Accept LCPF does not Granger Cause LGDP 142 0.60815 0.5458 Accept LGDP does not Granger Cause LCPF 5.36273 0.0057 Reject LINFL does not Granger Cause LGDP 142 0.56083 0.5720 Accept LGDP does not Granger Cause LINFL 0.39282 0.6759 Accept LMKC does not Granger Cause LGDP 142 2.93939 0.0562 Reject LGDP does not Granger Cause LMKC 9.91522 0.0001 Reject LCPF does not Granger Cause LBRVM 142 4.36648 0.0145 Reject LBRVM does not Granger Cause LCPF 0.47680 0.6218 Accept LINFL does not Granger Cause LBRVM 142 2.75601 0.0671 Reject LBRVM does not Granger Cause LINFL 0.12762 0.8803 Accept LMKC does not Granger Cause LBRVM 142 2.07924 0.1290 Accept LBRVM does not Granger Cause LMKC 3.26855 0.0411 Reject LINFL does not Granger Cause LCPF 142 1.95106 0.1461 Accept LCPF does not Granger Cause LINFL 0.08616 0.9175 Accept LMKC does not Granger Cause LCPF 142 0.31692 0.7289 Accept LCPF does not Granger Cause LMKC 2.45837 0.0893 Reject LMKC does not Granger Cause LINFL 142 0.14488 0.8653 Accept LINFL does not Granger Cause LMKC 2.94754 0.0558 Reject Source : Generated by Author According to Granger Causality, BRVM Composite Granger causes GDP with the inverse not being true. It simply says that a good performance of BRVM Composite index may have an effect on the Malian GDP and not the other way around. The results also indicated that there is a bidirectional relationship between Market Capitalization and GDP; Market Capitalization Granger causes GDP and at the same GDP Granger causes Market Capitalization. Moreover, the causality test unveils that GDP causes Capital Flow and not the inverse. Indeed, a high GDP indicated a good economic health and that attracts investor, or foreign direct investment. It has also been determined that Inflation has a unidirectional effect on Market Capitalization. 4. Conclusion This study explored the relationship between economic growth, as measured by GDP, and various macroeconomic variables, including Inflation, BRVM Market Capitalization, Capital Flow, and the BRVM Composite Index, using data from January 2009 to December 2020. Through rigorous analysis involving normality tests, stationarity tests, the ARDL model, VEC model, and Granger causality tests, several significant findings emerged. The results indicate that inflation and capital flow negatively impact GDP, whereas market capitalization and the BRVM Composite Index positively influence GDP, though their coefficients are not always significant at the 10% level. The long-run relationship analysis revealed that shocks in the short run are corrected in the long run, indicating the models' tendency to readjust towards equilibrium over time. The Granger causality test showed that the BRVM Composite Index influences GDP without the reverse being true, suggesting that the performance of the BRVM Composite Index can affect Mali's GDP. Additionally, a bidirectional relationship between market capitalization and GDP was identified, indicating mutual causality. GDP was also found to influence capital flow, underscoring the role of economic health in attracting foreign investments. Furthermore, inflation was shown to affect market capitalization unidirectionally. Based on these findings, several recommendations can be made for policymakers in Mali. To stimulate economic growth, it is essential to control inflation through effective monetary policies. Enhancing the performance and attractiveness of the BRVM Composite Index could positively influence GDP; these will call for a regional effort from all the WAEMU countries. Encouraging investments and improving market capitalization can create a virtuous cycle of growth. Additionally, fostering a stable and conducive economic environment is crucial for attracting capital flows and sustaining long-term economic health. Overall, this study underscores the interconnectedness of macroeconomic variables and their collective impact on economic growth, offering valuable insights for strategic economic planning in Mali. Declarations Availability of data and materials The data of this study were collected from different websites notably those of the World Bank, the IMF and the BRVM. The datasets are available Competing Interests The author has no conflicts of interest to declare. Funding Not applicable Authors' contributions The authors acknowledged their contribution to this study and approved it for publication. References Adjasi, C., & Biekpe, N. (2006). Stock Market Development and Economic Growth: The Case of Selected African Countries. African Development Review, 18, 144-161. https://doi.org/10.1111/J.1467-8268.2006.00136.X. Afonso, A., & Reimers, M. (2021). Does the Introduction of Stock Exchange Markets Boost Economic Growth in African Countries? Capital Markets: Market Microstructure eJournal. https://doi.org/10.1016/j.jce.2022.01.006. Arestis, P., Demetriades, P., & Luintel, K. (2001). Financial Development and Economic Growth: The Role of Stock Markets. Journal of Money, Credit and Banking, 33, 16-41. https://doi.org/10.2307/2673870. Baral, K. (2019). Effects of Stock Market Development on Economic Growth in Nepal. , 8, 87-96. https://doi.org/10.3126/jjis.v8i0.27302. Benhabib, J., & Spiegel, M. (2000). The Role of Financial Development in Growth and Investment. Journal of Economic Growth, 5, 341-360. https://doi.org/10.1023/A:1026599402490. Drebentsov, V., Bergsman, J., & Broadman, H. (1993). Finance and Growth: Schumpeter Might Be Right. . https://doi.org/10.2307/2118406. Durusu-Ciftci, D., Ispir, M., & Yetkiner, H. (2017). Financial development and economic growth: Some theory and more evidence. Journal of Policy Modeling, 39, 290-306. https://doi.org/10.1016/J.JPOLMOD.2016.08.001. Enisan, A., & Olufisayo, A. (2009). Stock market development and economic growth: Evidence from seven sub-Sahara African countries. Journal of Economics and Business, 61, 162-171. https://doi.org/10.1016/J.JECONBUS.2008.05.001. Eseosa, O., , Alex, O., , Barnabas, O., Henry, I. (2019). Correlation of stock market returns in the West African region from 2008 to 2016. Investment Management and Financial Innovations, 16(3), 120-130. doi:10.21511/imfi.16(3).2019.12. Available from: https://www.researchgate.net/publication/335329755_Correlation_of_stock_market_returns_in_the_West_African_region_from_2008_to_2016 [accessed Jun 10 2024]. Filer, R., Hanousek, J., & Campos, N. (2000). Do Stock Markets Promote Economic Growth?. ERN: Econometric Modeling in Macroeconomics (Topic). https://doi.org/10.2139/ssrn.1535900. Kagochi, J., & Durmaz, N. (2020). Stock market development, human capital, and economic growth in sub-Saharan Africa. Review of Development Finance, 10, 17-30. Levine, R., & Zervos, S. (1996). Stock market development and long-run growth. The World Bank Economic Review, 10, 323-339. https://doi.org/10.1093/WBER/10.2.323. Nazir, M., Nawaz, M., & Gilani, U. (2010). Relationship between economic growth and stock market development. African Journal of Business Management, 4, 3473-3479. https://doi.org/10.5897/AJBM.9000484. Pradhan, R. (2018). Development of stock market and economic growth: the G-20 evidence. Eurasian Economic Review, 8, 161-181. https://doi.org/10.1007/S40822-018-0094-4. S., A., & I.H., A. (2023). An Assessment of the Relationship between Stock market Development and Monetary Policy on Economic Growth in some selected African Countries. African Journal of Economics and Sustainable Development. https://doi.org/10.52589/ajesd-c31cncwv. Sustainable Stock Exchange Initiative. Retrieved from https://sseinitiative.org/stock-exchange/brvm. Tadesse, S. (2002). Financial Architecture and Economic Performance: International Evidence. Development Economics: Microeconomic Issues in Developing Economies eJournal. https://doi.org/10.2139/ssrn.307223. Titman, S., Wei, K., & Xie, F. (2013). Market Development and the Asset Growth Effect: International Evidence. Journal of Financial and Quantitative Analysis, 48, 1405 - 1432. https://doi.org/10.1017/S0022109013000495. Tsaurai, K. (2016). The Nexus Between Stock Market Development and Economic Growth. Corporate Ownership and Control, 14, 269-277. https://doi.org/10.22495/COCV14I1C1P10. Valderrama, D. (2003). Financial development, productivity, and economic growth. FRBSF Economic Letter. Vazakidis, A., & Adamopoulos, A. (2009). Stock Market Development and Economic Growth. American Journal of Applied Sciences, 6, 1932-1940. https://doi.org/10.3844/AJASSP.2009.1932.1940. Yu, J., Hassan, M., & Sanchez, B. (2012). A re-examination of financial development, stock markets development and economic growth. Applied Economics, 44, 3479-3489. https://doi.org/10.1080/00036846.2011.577019. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5067177","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":358082281,"identity":"7616980f-c3f8-4c3e-8f92-acc48b93b323","order_by":0,"name":"Boubacar Amadou CISSE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZgiVYMDAwAakbYCYsfEAKVrSQFoa8GthQNVyGMzDq4W/nf3xhw8Mdnnm7AfYHvyoOW+3tv0w0JYam2hcWiQO85hJzmBILrbsSWA37Dl2O3nbmUSglmNpuQ04tBgw87Ax8zAwJ244kMAmwdtwO9nsAFALY8NhPFrYH3/+w1CfuOH8AzbJvw3nks3OPySkhcFAGujrxA03EtikeRsO2JndIGAL2C89BseLLWc8bDeWOZacYHYDaEsCHr/w9x9//OFHRXWeOX/ysYdvauzszc6nP3zwocYGpxao80AEI1hNIphMwKscDdiTongUjIJRMApGBgAA8NpfVZ57bzcAAAAASUVORK5CYII=","orcid":"","institution":"Université des Sciences Sociales et de Gestion de Bamako","correspondingAuthor":true,"prefix":"","firstName":"Boubacar","middleName":"Amadou","lastName":"CISSE","suffix":""},{"id":358082282,"identity":"64cf137c-3920-4bfd-a2ce-e75569d052ad","order_by":1,"name":"Aminata TEME","email":"","orcid":"","institution":"Université des Sciences Sociales et de Gestion de Bamako","correspondingAuthor":false,"prefix":"","firstName":"Aminata","middleName":"","lastName":"TEME","suffix":""},{"id":358082283,"identity":"8d4bcaa9-3e8d-4bb4-9eb1-9910247e18d9","order_by":2,"name":"Salimou KEITA","email":"","orcid":"","institution":"Université des Sciences Sociales et de Gestion de Bamako","correspondingAuthor":false,"prefix":"","firstName":"Salimou","middleName":"","lastName":"KEITA","suffix":""},{"id":358082284,"identity":"f2af9848-6285-4bd9-8274-88dd229163e5","order_by":3,"name":"Yahya SÖNMEZ","email":"","orcid":"","institution":"Kastamonu Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Yahya","middleName":"","lastName":"SÖNMEZ","suffix":""}],"badges":[],"createdAt":"2024-09-10 23:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5067177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5067177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65241379,"identity":"89b7b7c8-c164-48aa-87b5-94c63553345d","added_by":"auto","created_at":"2024-09-25 06:49:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":926190,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5067177/v1/3f30b7bd-20dc-4acb-a9b3-9e03cda83b52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMacroeconomics Dynamics: Exploring the Impact of Stock Market Development on Economic Growth in Mali\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEconomic growth has been a central topic in economic discussions for decades. It is generally defined as an increase in the production of goods and services within an economy, typically measured by Gross Domestic Product (GDP). Numerous factors influence economic growth, with stock markets being a significant indicator of a country\u0026rsquo;s economic health. Valderrama (2023) found that financial development contributes to economic growth, but the precise mechanisms and policies needed to promote growth remain inconclusive. Benhabib and Spiegel (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) established that financial development positively impacts growth and investment, with indicators correlated to both total factor productivity growth and investment, but their correlations differ.\u003c/p\u003e \u003cp\u003eIn African economies, stock markets are still evolving and often require increased efficiency to attract more investors and companies. Despite their size, their impact on African economies cannot be overlooked, and the Regional Securities Exchange (Bourse R\u0026eacute;gionale des Valeurs Mobili\u0026egrave;res - BRVM) is a notable example.\u003c/p\u003e \u003cp\u003eThe BRVM serves as a regional exchange for eight West African countries, including the Republic of Mali. According to the World Bank (2024), Mali has a GDP growth rate of 3.5%, ranking 121st globally in terms of GDP (current US\u003cspan\u003e$\u003c/span\u003e) and 24th out of 47 Sub-Saharan African countries. Despite being considered \"mostly unfree\" with an economic freedom score below the global average but above the regional average (Index of Economic Freedom, 2024), Mali\u0026rsquo;s economy has shown resilience, maintaining slight growth amidst political and security challenges over the past decade.\u003c/p\u003e \u003cp\u003eMali, along with Cote d'Ivoire, Senegal, Burkina Faso, Niger, Togo, Benin, and Guinea-Bissau, shares the BRVM. The exchange is dominated by companies from Cote d'Ivoire and Senegal, the region's strongest economies. Many economic sectors are listed but health and technology companies are not.\u003c/p\u003e \u003cp\u003eCurrently, Mali has only one company listed in the BRVM's main compartment, the Bank of Africa Mali. Although this is a small presence among the 46 listed companies, various economic policies within the West African States Economic Community (ECOWAS) continue to promote economic integration and development (Sustainable Security Exchanges, 2023). Therefore, a regional economic integration is underway even though it still needs rooms for improvements.\u003c/p\u003e \u003cp\u003eThe question of \u0026ldquo;Does the regional stock exchange development affect Mali\u0026rsquo;s economy?\u0026rdquo; arises. This study investigates the existence of relationship between Mali\u0026rsquo;s economy growth and the BRVM development. In this study, research and publication ethics were complied with.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eStudies on economic growth and stock market development are not scarce among financial economics research topics. While most of them were conducted on developed economies markets, some are of international dimensions, and others are based on developing economies. Market development effects on asset growth is more noticeable in advance economies (Titman, Wei and Xie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, this effect is not related to trading expenses and corporate governance (Titman, Wei \u0026amp; Xie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther studies on the effect of stock market and banks on economic growth show that the former has more significant effect on economic growth than the latter (Arestis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). It is worth reminding that banks are also strong players in the stock markets. \u0026Ccedil;ift\u0026ccedil;i et al (2017) found similar results but stressed that credit market contribute more on the positive of stock market development on economic growth. Following Schumpeter\u0026rsquo;s assertion (1911) that financial intermediaries\u0026rsquo; core activities promote long term economic growth, Drebentsov, Bergsman and Broadman (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) established there is strong correlation between financial development and economic growth, investment rates and capital efficiency.\u003c/p\u003e \u003cp\u003eMost studies on developed nations have determined a positive relationship between market development and growth; this result was confirmed by Pradhan (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) from his research on G-20 countries. He further stressed the need for sustainable economic policies. Oppositive results were found in Belgium by Tsaurai (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) when he established the absence of long-term causality between stock market development and economic growth. However, Baral (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found a result in line with previous empirical studies in Nepal.\u003c/p\u003e \u003cp\u003eConclusive studies have also been conducted on emerging economies. El-Wassal (2008) carried out a study on 40 emerging economies from 1980 to the year 2000. He found that economic growth, financial freedom policies and foreign portfolio investment are the primary causes of stock market growth. Similarly, studies are scarce in Sub-Saharan African countries; Enisan and Olufisayo (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) analyzed the impact of market development to economic growth. Using a bound test and Vector Error Correlation Model, they found that stock market development cause economic growth in Egypt and South African. Their study further established that there is a two-directional Granger causal relationship between stock market development and economic growth in Cote d\u0026rsquo;Ivoire, Kenya, Morocco and Zimbabwe.\u003c/p\u003e \u003cp\u003eFurthermore, Adjasi and Biekpe (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) conducted a similar study on 14 African nations. They found a positive impact on stock market development on economic growth in most upper-middle-income economies and in moderately capitalized markets. The study leads to the conclusion that more developed and capitalized economies in Africa are in better position to drive countries to economic growth. Alfonso and Reimers (2021) investigated on the relationship between stock markets and economic growth in African countries; they found that the introduction of stock market leads to a hump-shaped increased in GDP per capita; the effect peaks in the fifth year and becomes insignificant afterward. They stressed that ongoing market development and supportive policies are necessary for sustainable results. Different results were found by Kagochi and Durmaz (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They conducted a study on the relationship between stock market development, human capital and economic growth on Sub-Saharan Africa. They found that human capital positively affects economic growth unlike stock market development. Similar research was carried out by Ali and Adahama (2023) on selected African economies with focus on market development, monetary policy and economic growth. They underscore that both market development and monetary policy have a positive effect on economic Algeria, Angola, Libya and Nigeria.\u003c/p\u003e \u003cp\u003eYu, Hassan and Sanchez (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found different causal relationship between stock market development and financial factors across different regions and income groups. Nazir, Nawaz and Gilani (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), on their study on Pakistan stock markets, established that the size and market capitalization can strongly influence economic growth. Filer, Hanousek and Campos (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) had found similar results but stressed that the effect can lead to currency appreciation.\u003c/p\u003e \u003cp\u003eIn his comparative study between bank-based versus market-based financial systems, Tadesse (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) observed that market-based systems are more effective in economies with developed financial sectors while bank-based systems have better performance in less developed financial environments. This indicates that countries\u0026rsquo; conditions determine the financial infrastructure.\u003c/p\u003e \u003cp\u003eIn Mali, as in the rest of west Africa region, financial integration is enhanced by the West Africa Economic and Monetary Union (WAEMU) through the creation of a regional stock exchange: Bourse Regionale des Valeurs Mobilieres (El-Wassal, 2008). El-Wassal (2008) further stressed that financial freedom policies and foreign portfolio investment play a crucial role in promoting stock market growth; he also determined that reducing entry barriers and enhancing market efficiency attract foreign investments.\u003c/p\u003e \u003cp\u003eThe results of El Wassal\u0026rsquo;s study also indicated that low country risk promotes stock market growth. Unlike country risk, high interest rate negatively affects stock market development (Vazakidis and Adamopoulos, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Levine and Zervos (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) had indicated that, whilst there is a positive correlation between economic development and stock development, the correlation is but partial and not definitive. This may lead to the understanding that stock market development can be influenced by many factors; political stability, regulatory quality and the overall economic environment could all play important role.\u003c/p\u003e \u003cp\u003eStudies on the correlation between stock market development and economic development have not always showed a positive correlation, in reference to Tsaurai (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Filer et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This leads to the conclusion that there is need for tailored policy approaches. Some argued that the effectiveness of financial systems is context-based and that policies meant for advocating the market and financial architecture must be adapted to each country\u0026rsquo;s specific conditions (Tadesse, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003eIn this section, data set, research methodology and empirical findings are presented.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Data Set\u003c/h2\u003e\n \u003cp\u003eThis study looks possible relationships between economic growth and other macroeconomic factors, notably Inflation, BRVM Market Capitalization, Capital Flow and BRVM Composite Index.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGross Domestic Product (GDP)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe economic well-being of a nation or region is frequently evaluated using Gross Domestic Product (GDP), which quantifies the total market worth of all final products and services generated and provided within a designated timeframe by one or multiple countries. It represents the economic growth in this study.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInflation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAs a widely used metric for gauging economic spending, the Consumer Price Index (CPI) serves as a valuable tool. In the context of this particular investigation, the CPI was employed to assess inflation levels.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMarket Capitalization\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe market capitalization of a publicly traded company represents the combined worth of all the common shares held by shareholders. This value is determined by multiplying the market price per common share by the total number of outstanding common shares.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCapital Flows\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThese include all transactions recorded in the capital accounts, as well as the financial account of the balance of payments, such as foreign direct investments (FDI), portfolio investments, and other investments including loans and banking capital.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBRVM Composite Index\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe BRVM Composite is a stock index calculated from the value of each stock on the BRVM. The BRVM Composite is a West African stock market index created with the base 100 on September 15, 1998. Monthly data from January 2009 to December 2020 were used to conduct this study. Data were collected from International Monetary Fund, World Bank and BRVM websites. Not all data were available in monthly data; those available in daily and annual frequences, were converted into monthly frequencies.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMacroeconomic Factors Used in Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariable\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIndicator\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMeasurement\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSource\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVariable Type\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic Growth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\text{G}\\text{D}\\text{P}}_{\\text{t}}-\\:{\\text{G}\\text{D}\\text{P}}_{\\text{t}-1}}{{\\text{G}\\text{D}\\text{P}}_{\\text{t}-1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{o}\\text{g}(\\frac{{\\text{G}\\text{D}\\text{P}}_{\\text{t}}}{{\\text{G}\\text{D}\\text{P}}_{\\text{t}-1}}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDependent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsumer Price Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{C}\\text{P}\\text{I}-\\:{\\text{C}\\text{P}\\text{I}}_{\\text{t}-1}}{{\\text{C}\\text{P}\\text{I}}_{\\text{t}-1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{o}\\text{g}\\left(\\frac{{\\text{C}\\text{P}\\text{I}}_{\\text{t}}}{{\\text{C}\\text{P}\\text{I}}_{\\text{t}-1}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational Monetary Fund\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStock Market Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRVM Composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\text{P}}_{\\text{t}}-\\:{\\text{P}}_{\\text{t}-1}}{{\\text{P}}_{\\text{t}-1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{o}\\text{g}\\left(\\frac{{\\text{P}}_{\\text{t}}}{{\\text{P}}_{\\text{t}-1}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarket Capitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarket Capitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\text{M}\\text{K}\\text{C}}_{\\text{t}}-\\:{\\text{M}\\text{K}\\text{C}}_{\\text{t}-1}}{{\\text{M}\\text{K}\\text{C}}_{\\text{t}-1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{o}\\text{g}\\left(\\frac{{\\text{M}\\text{K}\\text{C}}_{\\text{t}}}{{\\text{M}\\text{K}\\text{C}}_{\\text{t}-1}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCapital Flows\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapital Flow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\text{C}\\text{P}\\text{F}}_{\\text{t}}-\\:{\\text{C}\\text{P}\\text{F}}_{\\text{t}-1}}{{\\text{C}\\text{P}\\text{F}}_{\\text{t}-1}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{L}\\text{o}\\text{g}\\left(\\frac{{\\text{C}\\text{P}\\text{F}}_{\\text{t}}}{{\\text{C}\\text{P}\\text{F}}_{\\text{t}-1}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Research Methodology\u003c/h2\u003e\n \u003cp\u003eThis study explores the existence of relationship (long or short) between GDP and other variables (BRVM Composite, Market Capitalization, Capital flow and Inflation). Eviews 10 was used to perform the analyses. The generic research model determined is the following:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{G}\\text{D}\\text{P}}_{\\text{i}\\text{t}}={{\\beta\\:}}_{0}+\\:{{\\beta\\:}}_{1}{\\text{I}\\text{N}\\text{F}\\text{L}}_{\\text{i}\\text{t}}+\\:{{\\beta\\:}}_{2}{\\text{M}\\text{K}\\text{C}}_{\\text{i}\\text{t}}+\\:{{\\beta\\:}}_{3}{\\text{C}\\text{P}\\text{F}}_{\\text{i}\\text{t}}+\\:{{\\beta\\:}}_{4}{\\text{B}\\text{R}\\text{V}\\text{M}}_{\\text{i}\\text{t}}+\\:{{\\epsilon\\:}}_{\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eInflation sensitivity monthly change\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSensitivity of monthly change in Market Capitalization\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSensitivity of monthly change in Capital Flow\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSensitivity of monthly change in BRVM Composite\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eꜪ\u003csub\u003eit\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eError term\u003c/p\u003e\n \u003cp\u003eThe series normality test was first carried out. The variables logged values will be used to conduct the afterward-tests. The series stationarity test was then performed to detect a possible unit root; the test was run at both the Level and the First Difference. In the event where the series are I(0) and I(1), the Bound Test statistics and not the Johansen Test will be used for cointegration. The Bound Test is more appropriate because it is applicable on series at I(0) and I(1) and is more suitable for small and finite data samples. In the case of a Level relationship the variables, it will indicate that there are both short-run and long-run relationships between the variables. An Autoregressive Distributed Lag (ARDL) and a Vector Error Correction (VEC) models will be determined. The Error Correct model to be estimated is as follows:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{\\text{G}\\text{D}\\text{P}}_{\\text{T}}=\\:{{\\gamma\\:}}_{0}+\\:\\sum\\:_{\\text{i}=0}^{\\text{p}}{{\\gamma\\:}}_{1}{\\varDelta\\:\\text{I}\\text{N}\\text{F}\\text{L}}_{\\text{t}-1}+\\:\\sum\\:_{\\text{i}=0}^{\\text{P}}{{\\phi\\:}}_{\\text{i}}{CPF}_{\\text{t}-1}+\\:\\sum\\:_{\\text{i}=0}^{\\text{P}}{{\\phi\\:}}_{\\text{i}}{MKC}_{\\text{t}-1}+\\:\\sum\\:_{\\text{i}=0}^{\\text{P}}{{\\phi\\:}}_{\\text{i}}{BRVM}_{\\text{t}-1}\\:+\\:{{\\mu\\:}\\text{E}\\text{C}\\text{T}}_{\\text{t}-1}+\\:{\\text{u}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gamma;\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003e1\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e and \u003cstrong\u003e\u0026phi;i\u003c/strong\u003e stand for short-term coefficients, as for \u003cstrong\u003e∆\u003c/strong\u003e, it represents the difference operator, \u003cstrong\u003e\u0026micro;\u003c/strong\u003e stands for the order of delay, u\u003csub\u003ei\u003c/sub\u003e represent the residuals and ECT\u003csub\u003et\u0026minus;1\u003c/sub\u003e signifies the term for error correction.\u003c/p\u003e\n \u003cp\u003eThe ECT is expressed as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\text{G}\\text{D}\\text{P}}_{\\text{t}-1}=\\:{{\\delta\\:}}_{1}{\\text{I}\\text{N}\\text{F}\\text{L}}_{\\text{t}-1}+\\:{{\\delta\\:}}_{2}{\\text{C}\\text{P}\\text{F}}_{\\text{t}-1}+\\:{{\\delta\\:}}_{3}{\\text{M}\\text{K}\\text{C}}_{\\text{t}-1}+\\:{{\\delta\\:}}_{4}{\\text{B}\\text{R}\\text{V}\\text{M}}_{\\text{t}-1}+\\:{{\\epsilon\\:}}_{1}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe long-term association between variables is displayed by ECT. The rate at which stock returns revert to equilibrium following a long-term divergence is measured by the \u003cstrong\u003eu\u003c/strong\u003e coefficient. The system is considered balanced if the error correction coefficient is less than 1, and when it is negatively indicated, it suggests that, in the event of a deviation from balance, there is a movement towards equilibrium. In other words, according to Bozkurt (2007: 166), the mistake correction process functions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Empirical Findings\u003c/h2\u003e\n \u003cp\u003eThe result for the normality test is as follows\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNormality Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCAPITAL_FLOW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBRVM_COMP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eINFLATION_CHANGE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMARKET_CAP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.889118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.975233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.337083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.754179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.111801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.041667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.084700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.971522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.560000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.353846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.235500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.003936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.110900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.710000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.115385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.225470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.358703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.721538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.190079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.549780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.565118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.102936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.282130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.454369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.828300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.692086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.643682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJarque-Bera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.58682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.653401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.37067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.233467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.016077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.601675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e560.0330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e428.4336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.062800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48.54000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.25895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum Sq. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e708.2384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.6519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e795.5778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.472739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe skewness\u0026rsquo; different values are all close to zero (0), which is the normal value for skewness for a normally distributed series. The Kurtosis, that displays the peak of flatness of the distribution of a series, has the value 3 as the normal Kurtosis value for a normally distributed series; the different series in this study have kurtosis values close to 3. Theses give enough idea that the series are normally distributed.\u003c/p\u003e\n \u003cp\u003eThe Jarque-Bera statistics measure the difference the skewness the kurtosis of the variables. The null hypothesis states that the distribution is normal. At 1% significance level, it is safe to accept the null hypothesis. After this test, the logged values of the series were used to perform further tests.\u003c/p\u003e\n \u003cp\u003eNext, the series stationarity test was performed, and the relative results are as follow:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnit Root Test for Stationarity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eUNIT ROOT TEST RESULTS TABLE (ADF)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eNull Hypothesis: the variable has a unit root\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAt Level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLINFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith Constant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.4177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.9836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.3408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.1960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1387\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1609\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0223\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.6272\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith Constant \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.3991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.0415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.2295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.3079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3785\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.4690\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0691\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9737\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithout Constant \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3889\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.6906\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3259\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.6319\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.4413\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAt First Difference\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed(LGDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed(LBRVM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed(LCPF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed(LINFL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ed(LMKC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith Constant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.1972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.8691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.4405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5816\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith Constant \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.8046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.3427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.8783\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithout Constant \u0026amp; Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.2155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.8855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.4638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1753\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0105\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNotes\u003c/span\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ea: (*)Significant at the 10%; (**)Significant at the 5%; (***) Significant at the 1% and (no) Not Significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eb: Lag Length based on AIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ec: Probability based on MacKinnon (1996) one-sided p-values.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe stationarity test depicts that some variables (BRVM and Inflation) are stationary at Level (I(0)) and the rests are I(1). It has been noticed that all the variables are stationary at first difference apart from Capital Flow.\u003c/p\u003e\n \u003cp\u003eThe result for the Bound Test for cointegration is as displayed below;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBound Test for Cointegration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF-Bounds Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNull Hypothesis: No levels relationship\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignif.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsymptotic: n\u0026thinsp;=\u0026thinsp;1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.993358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActual Sample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinite Sample: n\u0026thinsp;=\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe F-statistics value is superior to the level value (I(0)) value. This leads to the conclusion that there is enough significance (5%) to reject the null hypothesis of no long-run correlation between the variables. In other others, there is a long-run relationship between the variables.\u003c/p\u003e\n \u003cp\u003eBoth the long and short run models need to be estimated; appropriate techniques are the ARDL and VEC models.\u003c/p\u003e\n \u003cp\u003eAfter running the ARDL models, it is necessary to determine the lag length for the afterward-analyses.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLag selection\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLag\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHQ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-386.7358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.760821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.867904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.804337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e679.2595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2037.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.91e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.547933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.905436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.286839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e990.2493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e571.6725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33e-13*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.75367*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.57575*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.27499*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1010.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.19851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.93e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.67934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.96601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.98309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1018.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.29610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.43601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.18727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.52218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1053.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.80269*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.96e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.57566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.79150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.44425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1065.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.77595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.39635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.07678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.04736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1076.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.55843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.18429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.329306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.61772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1082.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.872365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.91004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.519637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.12589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs the most decision criteria (including Akaike Information Criteria - AIC), the optimal lag length is 2.\u003c/p\u003e\n \u003cp\u003eThe resulting ARDL model is determined below:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eARDL Model \u0026ndash; Short Run Relationship\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGDP(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.561969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.35951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGDP(-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.579662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.002134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.358929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.261641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.371839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.547928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.087395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLINFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.034949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.204529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.689745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.545708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.096425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCPF(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.029527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.041353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.909222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCPF(-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.403177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.542557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.586228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLBRVM(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.118675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.889243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLBRVM(-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.026680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.679693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eS.D. dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS.E. of regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAkaike info criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.392089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum squared resid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSchwarz criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.142301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323.8383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHannan-Quinn criter.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.290585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e893.0384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDurbin-Watson stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.066688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb(F-statistic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e*Note: p-values and any subsequent tests do not account for model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eselection.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe results show that 98.58% of change in GDP is explained by the regressors. Inflation and Capital Flow, with enough significance level, have both negative effects on GDP. The results also show that Market Capitalization and BRVM Composite index have positive influence on GDP; however, these variables do not have their coefficients significant at 10%.\u003c/p\u003e\n \u003cp\u003eThe long-run relationship results depict the following\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVEC Model \u0026ndash; Long Run Relationship\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LGDP(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.63024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LBRVM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LBRVM(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.232597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.014544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LCPF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.513056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.547869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LCPF(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.412658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.380318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LINFL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.461455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LINFL(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.736380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LMKC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.261334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.553963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD(LMKC(-1))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.570278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.765552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECT(-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.022552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.695332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eS.D. dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS.E. of regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAkaike info criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.336057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum squared resid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSchwarz criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.107085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318.8601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHannan-Quinn criter.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.243012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.31015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDurbin-Watson stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.157282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb(F-statistic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFollowing the results, it can be noticed that there is an ECT value of -0.0225. the negative sign indicates that shocks in the short run will be corrected in the long run at 10% significance level. In other words, the models will re-adjust itself in the long run after a shock in the long.\u003c/p\u003e\n \u003cp\u003eTo understand the nature of the relationship between the variables, the Granger Causality test was conducted, and the results are as depicted below. The null hypothesis for the test states that A does not Granger cause B.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGranger Causality Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNull Hypothesis:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecision\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLBRVM does not Granger Cause LGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.56159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLGDP does not Granger Cause LBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCPF does not Granger Cause LGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLGDP does not Granger Cause LCPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLINFL does not Granger Cause LGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLGDP does not Granger Cause LINFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC does not Granger Cause LGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLGDP does not Granger Cause LMKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.91522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLCPF does not Granger Cause LBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.36648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLBRVM does not Granger Cause LCPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLINFL does not Granger Cause LBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.75601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLBRVM does not Granger Cause LINFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC does not Granger Cause LBRVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLBRVM does not Granger Cause LMKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLINFL does not Granger Cause LCPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLCPF does not Granger Cause LINFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC does not Granger Cause LCPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLCPF does not Granger Cause LMKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLMKC does not Granger Cause LINFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLINFL does not Granger Cause LMKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReject\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: \u003cem\u003eGenerated by Author\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAccording to Granger Causality, BRVM Composite Granger causes GDP with the inverse not being true. It simply says that a good performance of BRVM Composite index may have an effect on the Malian GDP and not the other way around. The results also indicated that there is a bidirectional relationship between Market Capitalization and GDP; Market Capitalization Granger causes GDP and at the same GDP Granger causes Market Capitalization.\u003c/p\u003e\n \u003cp\u003eMoreover, the causality test unveils that GDP causes Capital Flow and not the inverse. Indeed, a high GDP indicated a good economic health and that attracts investor, or foreign direct investment. It has also been determined that Inflation has a unidirectional effect on Market Capitalization.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study explored the relationship between economic growth, as measured by GDP, and various macroeconomic variables, including Inflation, BRVM Market Capitalization, Capital Flow, and the BRVM Composite Index, using data from January 2009 to December 2020. Through rigorous analysis involving normality tests, stationarity tests, the ARDL model, VEC model, and Granger causality tests, several significant findings emerged.\u003c/p\u003e \u003cp\u003eThe results indicate that inflation and capital flow negatively impact GDP, whereas market capitalization and the BRVM Composite Index positively influence GDP, though their coefficients are not always significant at the 10% level. The long-run relationship analysis revealed that shocks in the short run are corrected in the long run, indicating the models' tendency to readjust towards equilibrium over time.\u003c/p\u003e \u003cp\u003eThe Granger causality test showed that the BRVM Composite Index influences GDP without the reverse being true, suggesting that the performance of the BRVM Composite Index can affect Mali's GDP. Additionally, a bidirectional relationship between market capitalization and GDP was identified, indicating mutual causality. GDP was also found to influence capital flow, underscoring the role of economic health in attracting foreign investments. Furthermore, inflation was shown to affect market capitalization unidirectionally.\u003c/p\u003e \u003cp\u003eBased on these findings, several recommendations can be made for policymakers in Mali. To stimulate economic growth, it is essential to control inflation through effective monetary policies. Enhancing the performance and attractiveness of the BRVM Composite Index could positively influence GDP; these will call for a regional effort from all the WAEMU countries. Encouraging investments and improving market capitalization can create a virtuous cycle of growth. Additionally, fostering a stable and conducive economic environment is crucial for attracting capital flows and sustaining long-term economic health.\u003c/p\u003e \u003cp\u003eOverall, this study underscores the interconnectedness of macroeconomic variables and their collective impact on economic growth, offering valuable insights for strategic economic planning in Mali.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study were collected from different websites notably those of the World Bank, the IMF and the BRVM. The datasets are available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledged their contribution to this study and approved it for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdjasi, C., \u0026amp; Biekpe, N. (2006). Stock Market Development and Economic Growth: The Case of Selected African Countries. African Development Review, 18, 144-161. https://doi.org/10.1111/J.1467-8268.2006.00136.X.\u003c/li\u003e\n\u003cli\u003eAfonso, A., \u0026amp; Reimers, M. (2021). Does the Introduction of Stock Exchange Markets Boost Economic Growth in African Countries? Capital Markets: Market Microstructure eJournal. https://doi.org/10.1016/j.jce.2022.01.006.\u003c/li\u003e\n\u003cli\u003eArestis, P., Demetriades, P., \u0026amp; Luintel, K. (2001). Financial Development and Economic Growth: The Role of Stock Markets. Journal of Money, Credit and Banking, 33, 16-41. https://doi.org/10.2307/2673870.\u003c/li\u003e\n\u003cli\u003eBaral, K. (2019). Effects of Stock Market Development on Economic Growth in Nepal. , 8, 87-96. https://doi.org/10.3126/jjis.v8i0.27302.\u003c/li\u003e\n\u003cli\u003eBenhabib, J., \u0026amp; Spiegel, M. (2000). The Role of Financial Development in Growth and Investment. Journal of Economic Growth, 5, 341-360. https://doi.org/10.1023/A:1026599402490.\u003c/li\u003e\n\u003cli\u003eDrebentsov, V., Bergsman, J., \u0026amp; Broadman, H. (1993). Finance and Growth: Schumpeter Might Be Right. . https://doi.org/10.2307/2118406.\u003c/li\u003e\n\u003cli\u003eDurusu-Ciftci, D., Ispir, M., \u0026amp; Yetkiner, H. (2017). Financial development and economic growth: Some theory and more evidence. Journal of Policy Modeling, 39, 290-306. https://doi.org/10.1016/J.JPOLMOD.2016.08.001.\u003c/li\u003e\n\u003cli\u003eEnisan, A., \u0026amp; Olufisayo, A. (2009). Stock market development and economic growth: Evidence from seven sub-Sahara African countries. Journal of Economics and Business, 61, 162-171. https://doi.org/10.1016/J.JECONBUS.2008.05.001.\u003c/li\u003e\n\u003cli\u003eEseosa, O., , Alex, O., , Barnabas, O., Henry, I. (2019). Correlation of stock market returns in the West African region from 2008 to 2016. Investment Management and Financial Innovations, 16(3), 120-130. doi:10.21511/imfi.16(3).2019.12. Available from: https://www.researchgate.net/publication/335329755_Correlation_of_stock_market_returns_in_the_West_African_region_from_2008_to_2016 [accessed Jun 10 2024].\u003c/li\u003e\n\u003cli\u003eFiler, R., Hanousek, J., \u0026amp; Campos, N. (2000). Do Stock Markets Promote Economic Growth?. ERN: Econometric Modeling in Macroeconomics (Topic). https://doi.org/10.2139/ssrn.1535900.\u003c/li\u003e\n\u003cli\u003eKagochi, J., \u0026amp; Durmaz, N. (2020). Stock market development, human capital, and economic growth in sub-Saharan Africa. Review of Development Finance, 10, 17-30.\u003c/li\u003e\n\u003cli\u003eLevine, R., \u0026amp; Zervos, S. (1996). Stock market development and long-run growth. The World Bank Economic Review, 10, 323-339. https://doi.org/10.1093/WBER/10.2.323.\u003c/li\u003e\n\u003cli\u003eNazir, M., Nawaz, M., \u0026amp; Gilani, U. (2010). Relationship between economic growth and stock market development. African Journal of Business Management, 4, 3473-3479. https://doi.org/10.5897/AJBM.9000484.\u003c/li\u003e\n\u003cli\u003ePradhan, R. (2018). Development of stock market and economic growth: the G-20 evidence. Eurasian Economic Review, 8, 161-181. https://doi.org/10.1007/S40822-018-0094-4.\u003c/li\u003e\n\u003cli\u003eS., A., \u0026amp; I.H., A. (2023). An Assessment of the Relationship between Stock market Development and Monetary Policy on Economic Growth in some selected African Countries. African Journal of Economics and Sustainable Development. https://doi.org/10.52589/ajesd-c31cncwv.\u003c/li\u003e\n\u003cli\u003eSustainable Stock Exchange Initiative. Retrieved from https://sseinitiative.org/stock-exchange/brvm.\u003c/li\u003e\n\u003cli\u003eTadesse, S. (2002). Financial Architecture and Economic Performance: International Evidence. Development Economics: Microeconomic Issues in Developing Economies eJournal. https://doi.org/10.2139/ssrn.307223.\u003c/li\u003e\n\u003cli\u003eTitman, S., Wei, K., \u0026amp; Xie, F. (2013). Market Development and the Asset Growth Effect: International Evidence. Journal of Financial and Quantitative Analysis, 48, 1405 - 1432. https://doi.org/10.1017/S0022109013000495.\u003c/li\u003e\n\u003cli\u003eTsaurai, K. (2016). The Nexus Between Stock Market Development and Economic Growth. Corporate Ownership and Control, 14, 269-277. https://doi.org/10.22495/COCV14I1C1P10.\u003c/li\u003e\n\u003cli\u003eValderrama, D. (2003). Financial development, productivity, and economic growth. FRBSF Economic Letter.\u003c/li\u003e\n\u003cli\u003eVazakidis, A., \u0026amp; Adamopoulos, A. (2009). Stock Market Development and Economic Growth. American Journal of Applied Sciences, 6, 1932-1940. https://doi.org/10.3844/AJASSP.2009.1932.1940.\u003c/li\u003e\n\u003cli\u003eYu, J., Hassan, M., \u0026amp; Sanchez, B. (2012). A re-examination of financial development, stock markets development and economic growth. Applied Economics, 44, 3479-3489. https://doi.org/10.1080/00036846.2011.577019.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Macroeconomics Dynamics, Economic Growth, Stock Market Development","lastPublishedDoi":"10.21203/rs.3.rs-5067177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5067177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEconomic growth is often influenced by various factors, with stock market development playing a crucial role. This study investigates the relationship between the stock market development, specifically the Bourse Régionale des Valeurs Mobilières (BRVM), and economic growth in Mali. The BRVM serves eight West African countries, including Mali, and its development may impact regional economies. Utilizing monthly data from January 2009 to December 2020, this research examines the effects of BRVM Market Capitalization, BRVM Composite Index, Capital Flow, and Inflation on Mali's Gross Domestic Product (GDP). The study employs an Autoregressive Distributed Lag (ARDL) model and Vector Error Correction (VEC) model to analyze both short-run and long-run relationships. Findings suggest a significant long-run relationship between stock market development and economic growth in Mali, with inflation and capital flow having negative impacts, while market capitalization and the BRVM Composite Index show positive but less significant effects. The study contributes to understanding the macroeconomic dynamics within West African economies and the importance of stock market development in promoting economic growth.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGel: E02, E20, O10, F43\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Macroeconomics Dynamics: Exploring the Impact of Stock Market Development on Economic Growth in Mali","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-25 06:41:27","doi":"10.21203/rs.3.rs-5067177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50d771f9-965d-40fa-ac30-906906da50b7","owner":[],"postedDate":"September 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-25T06:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-25 06:41:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5067177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5067177","identity":"rs-5067177","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}