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\begin{document}

             \author[1]{Tanvir Hasan  Anik}

             \author[2]{Md. Nurul Kabir  Biplob}

             \affil[1]{  Begum Rokeya University}

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\date{\small \em Received: 7 December 2018 Accepted: 31 December 2018 Published: 15 January 2019}

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\begin{abstract}
        


This study aims to investigate the effect of some macroeconomic variables namely; Interest rate, Real exchange Rate, Money supply (M2) and Trade Openness on economic growth in Bangladesh. The economic growth was represented by real gross domestic product and interest rate was presented by real interest rate in Bangladesh, Real Exchange Rate was presented by Real effective exchange rate and M2 was the proxy of money supply. The periods taken for the study were from year 1987 to year 2017. The study has been conducted using Augmented Dickey-Fuller Unit Root Test, Phillips Perron Test, Johansen Cointegration Test, Vector Error correction Model and Wald Test. The findings of the Study is, there have long run relationship coming from Real exchange rate, Interest rate, Money supply (M2) and Trade openness to economic growth. Besides these, there have casual short term relationship coming from Trade openness and Money supply (M2) to economic growth in Bangladesh.

\end{abstract}


\keywords{causality test, cointegration test, economic development, M2, trade openness, unit root test.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
here are many macroeconomic variables that can affect economic growth. Sometimes in long term perspective and sometimes in short run perspective. This study is with four macroeconomic variables namely; interest rate, real exchange rate, money supply and trade openness. Like Bangladesh, the developing countries are very much affected by these macroeconomic variables. Exchange rate has momentous impact on economy. Because exchange rate appreciation and depreciation maintains an economy's inflow and outflow of funds and affect every transactions to some extent. Basically balance of payment is affected by exchange rate fluctuation. It may be positively affected or negatively. Another macroeconomic variable is interest rate. It is a vital part of an economy. Interest rate affects the investment, which in turn affect the economy's growth. Lower interest rate demotivated the foreign investors to invest with this lower compensation. For this lower compensation foreign investment from a country may flight. Thus, investment may reduce, which reduces the employment and income as well. Money supply (M2) is another macroeconomic variable. Increasing money supply may increase inflation or price level of the economy if reasonable production may not happen at that period. And M2 is the proxy of money supply. It is broad money. Money supply, to some extent, may be good when economic production is in reasonable. Another macroeconomic variable used in this study is trade openness or trade liberalization. From the very primitive era it is known to all that no one good at everything. He who good at what, he should produce that. By this procedure the world can increase their total output. In narrow mind by removing trade barriers with other countries, an economy may achieve their goal easily with effortless manner. Basically trade openness is needed to run after considering local traders interest. 
\section[{II.}]{II.} 
\section[{Objectives of the Study a) General Objectives}]{Objectives of the Study a) General Objectives}\par
The main objectives of this study will be to explore the relationship between some macroeconomic variables namely; Interest rate, Real exchange Rate, Money supply, Trade Openness and economic growth in Bangladesh. 
\section[{b) Research Objectives}]{b) Research Objectives}\par
Our study aims to address the following objectives: a) To identify whether there have long run relationship among Interest rate, Real exchange Rate, Money supply, Trade Openness and economic growth in Bangladesh. b) To identify whether there have short run relationship or casual relationship among Interest rate, Real exchange Rate, Money supply, Trade Openness and economic growth in Bangladesh. 
\section[{c) Research Questions}]{c) Research Questions}\par
In order to achieve the above-mentioned research objectives, some answers are required for the following: a) Is there any short run impact of the changes of interest rate on economic growth? b) Is there any long run impact of the changes of interest rate on economic growth? c) Is there any short run impact of the changes of money supply on economic growth? d) Is there any long run impact of the changes of money Supply on economic growth? e) Is there any short run impact of the changes of real exchange rate on economic growth? f) Is there any long run impact of the changes of real exchange rate on economic growth? g) Is there any short run impact of the changes of trade Openness on economic growth? h) Is there any long run impact of the changes of trade Openness on economic growth?\par
III. 
\section[{Literature Review}]{Literature Review}\par
The literature review consists of four parts. These are given below. 
\section[{a) Relationship between Exchange Rate and Economic Growth}]{a) Relationship between Exchange Rate and Economic Growth}\par
The larger the exchange rate, the poorer will be the condition of the importing country and vice versa. In such a case, there is no doubt to say that exchange rate has a direct effect on the economic growth of any country \hyperref[b11]{(Kendal, 2004)}.Depreciation of the currency leads to increase in real output. There has been a significant correlation between inward FDI in the United States and the US real exchange rates since1970. \hyperref[b23]{(Rosengren, 1992)}. When exchange rate drops by 1\%, the stock market will react with less than 1\%drop. US should maintain policy to strengthen return through appreciate currency value \hyperref[b1]{(Anokye \& George, 2007)}. However, investor responds more heavily during a decrease in the exchange rate losses than an increase in the exchange rate profits \hyperref[b9]{(Jevando , Lius, 2004)}. Sometimes perhaps all developing economy like South Asian countris are wholly dependent on exchange rate. \hyperref[b4]{(Chong \& Tan, 2007)}. Real exchange rate affect relative cost of production and relative exchange rates affect the relative wealth significantly across countries and relative wealth significantly affects foreign direct investment in the united states and the effects of real wages have little impact on Foreign direct investment \hyperref[b2]{(Arizonan, 1992)}.\par
Exchange rate fluctuations have momentous implications for economic performance in Bangladesh. This is why fluctuation in exchange rate brings about changes in trade balance by influencing the country's export and import. Exchange rates may reason the price level to change and, as a consequence, it may change the income and wealth distribution of the economy. \hyperref[b7]{(Jaher, 2007)} Export-import-led growth for Bangladesh in both the long run and short run by using a dynamic panel data analysis, that existence of having cointegration, that is, stable long-run relationship between trade balance of Bangladesh and its determinants \hyperref[b8]{(Jakir, 2006)}. 
\section[{b) Relationship between Interest Rate and Economic Growth}]{b) Relationship between Interest Rate and Economic Growth}\par
According to traditional economic perspective, interest rate has negative effect on Economic growth. When the interest rate is high, investors tries to swing their money from stock market to savings or fixed deposit accounts. Because stock market become higher risk sector, Which lead to effect on economic growth negatively ).In the Long-run, interest rate has negative effect on the Economic growth in Japan \hyperref[b19]{(Mukherjee \& Naka, 1995)}. Using daily data showed that the stock returns are heavily sensitive to interest rate and exchange rate changes \hyperref[b10]{(Joseph \& Vezos , 2006)}.Higher interest rate encourage the foreign investors to invest, which in turn lead to economic growth .\par
Cointegrating relationship between two variables: interest rate and GDP \hyperref[b20]{(Nikiforos, 2010)}. On the other hand, there is insignificant relationship between Japanese economic growth and interest rate, especially the domestic interest rate. This is because the interest rate in Japan has implemented unprecedented monetary easing, falling the interest rate to almost zero, thus interest rate can affect the economic growth at all \hyperref[b13]{(Kurihara \& Nezu , 2006)}. In therefore plan and control the interest rate to help the growth of economy \hyperref[b21]{(Ologunde, 2007)}. 
\section[{c) Relationship between Money supply and Economic Growth}]{c) Relationship between Money supply and Economic Growth}\par
The probability of using M2 to target the quarterly rate of growth of nominal GDP in their paper in 1994. The study manifested that the Federal Reserve could perhaps make use of M2 that diminishes both the long-term average inflation rate and the fluctuation of annual GDP growth rate \hyperref[b16]{(Liu \& shrestha, 2008)}.Money supply has positive long run relationship with the economic development \hyperref[b22]{(Ramin \& Chuin, 2005)}. 
\section[{d) Relationship between Trade Openness and Economic Growth}]{d) Relationship between Trade Openness and Economic Growth}\par
It is showed that trade openness and exchange rate has a significantly positive impact on economic growth \hyperref[b24]{(Sacha \& Diamond, 2016)}. There is a significant positive relationship between openness and productivity growth \hyperref[b6]{(Iqbal, 1998)}. A positive relationship between openness and economic growth \hyperref[b0]{(Abdullah, 2013)}.\par
A multiple regression framework to investigate macroeconomics determinants of growth in pakistan including openness. The results suggest that openness has a beneficial effect on economic growth \hyperref[b25]{(Zahid, 2007)}. Open trade regimes force greater dependence on the market. Empirical evidence on the positive impact of liberalization on growth is quite abundant \hyperref[b5]{(Dollar, 2005)}.\par
IV. 
\section[{Data and Methodology a) Sample Description}]{Data and Methodology a) Sample Description}\par
The study uses annual time series data for Bangladesh during 1987-2017. The data are collected from Bangladesh Bank and World Bank. All these sources of data are recognized, accepted and the provided information that has been used widely in the country. So data and information of the sources incorporated in this analysis are reliable. 
\section[{b) Variables}]{b) Variables}\par
The study is completed with the help of some dependent and independent variables. Here the dependent variable is Real GDP taken as a proxy of economic growth. Whereas, the independent variables are real interest rate taken as a proxy of interest rate in Bangladesh, M2 is taken as a proxy of Money supply. Besides these, there have another two independent variables. These are Real exchange rate and Trade openness. 
\section[{Figure 1 i. Real Interest Rate}]{Figure 1 i. Real Interest Rate}\par
A real interest rate refers to an interest rate that has been adjusted to remove the effects of inflation to reflect the real cost of funds to the borrower and the real yield to the lender or to an investor. The real interest rate of an investment is measured as the amount by which the nominal interest rate is higher than the inflation rate:\par
Real Interest Rate= Nominal Interest Rate-Inflation (Expected or Actual). 
\section[{ii. Money Supply}]{ii. Money Supply}\par
In any economic system, the definition of something as seemingly straightforward as "money" can be surprisingly elusive. "Money" likely has very different meanings for an individual investor, a large financial firm, and a central bank or government, for instance. From the perspective of the study of economics, the money supply of a particular economy is equal to the total value of all monetary assets available within that economy. Central banks watch carefully over the money supply of a country to guard against issues like longterm price inflation, which often comes about as a result of rapid growth of the money supply of a country.\par
Measuring the money supply of an economy is a challenging proposition. Due to the complexity of the concept of "money," as well as the size and level of detail of an economy, there are multiple ways of measuring a money supply. These means of measuring a money supply are typically classified as "M"s and fall along a spectrum from narrow to broad monetary aggregates. Typically, the "M"s range from M0 to M3, with M2 typically representing a fairly broad measure. 
\section[{iii. Trade Openness}]{iii. Trade Openness}\par
Trade openness refers to the outward or inward orientation of a given country's economy. Outward orientation refers to economies that take significant advantage of the opportunities to trade with other countries. A common measure is the openness index, which adds imports and exports in goods and services and divides this sum by GDP. The larger the ratio, the more the country is exposed to international trade. For small economies that cannot produce everything they need, more trade has to be external. 
\section[{Real Effective Exchange Rate}]{Real Effective Exchange Rate}\par
Exchange rates of Taka for inter-bank and customer transactions are set by the dealer banks, based on demand-supply interaction. Bangladesh Bank (BB) is not in the market on a day-to-day basis, and undertakes USD purchase or sale transactions with dealer banks at prevailing inter-bank exchange rates only as needed to maintain orderly market conditions.\par
Inter-bank exchange rates are also used by BB for purchase and sale transactions with the Government and different International Organizations. The USD/BDT buying and selling rates below are highest and lowest inter-bank exchange rates at Dhaka. The cross rates of BDT with other foreign currencies are based on NY and Dhaka closing exchange rates.\par
The real effective exchange rate (REER) is the weighted average of a country's currency in relation to an index or basket of other major currencies, adjusted for the effects of inflation. The weights are determined by comparing the relative trade balance of a country's currency against each country within the index. This exchange rate is used to determine an individual country's currency value relative to the other major currencies in the index, such as the U.S. dollar, Japanese yen and the euro. The real effective exchange rate (REER) is used to measure the value of a specific currency in relation to an average group of major currencies. The REER takes into account any changes in relative prices and shows what can actually be purchased with a currency. This means that the REER is normally trade-weighted .The REER is derived by taking a country's nominal effective exchange rate (NEER) and adjusting it to include price indices and other trends. The REER, then, is essentially a country's NEER after removing price inflation or labor cost inflation. The REER represents the value that an individual consumer pays for an imported good at the consumer level. This rate includes any tariffs and transaction costs associated with importing the good. A country's REER can also be derived by taking the average of the bilateral real exchange rates (RER) between itself and its trading partners and then weighing it using the trade allocation of each partner. Regardless of the way in which REER is calculated, it is an average and considered in equilibrium when it is overvalued in relation to one trading partner and undervalued in relation to a second partner.\par
v. Gross Domestic Product Economic growth is the increase in the inflationadjusted market value of the goods and services produced by an economy over time. It is conventionally measured as the percent rate of increase in real gross domestic product, or real GDP. To calculate annualized GDP growth rates, start by finding the GDP for 2 consecutive years. Then, subtract the GDP from the first year from the GDP for the second year. Finally, divide the difference by the GDP for the first year to find the growth rate. Remember to express your answer as a percentage. An economy producing below its potential GDP most often results from A High prices of production inputs. B corrupt government policies that stifle economic growth. low consumer demand for goods and services in the economy. An economy producing below its potential GDP most often results from A High prices of production inputs. B corrupt government policies that stifle economic growth. Economists traditionally use gross domestic product (GDP) to measure economic progress. If GDP is rising, the economy is in good shape, and the nation is moving forward. If GDP is falling, the economy is in trouble, and the nation is losing ground. 
\section[{Variables}]{Variables}\par
Description  
\section[{c) Model Specification}]{c) Model Specification}\par
To examine the relationship between some macroeconomic variables (Interest rate, Real exchange Rate, Money supply, Trade Openness) and economic growth in Bangladesh, we have specified following econometric model. The independent variables are interest rate, M2, Real exchange Rate and Trade Openness, while the dependent variable is GDP growth as a proxy of economic growth.\par
Sometimes it is needed to convert the time series data into logarithm .The purpose of log-linear (L) is because of the value of some variables such as gross domestic product in US Dollars, while the Real interest rates are in percentage. There was therefore the use of logarithm in the model to bring the variables to the same base since the variables were measured in different ways. Using Schwarz Information Criterion (SIC), the lag length is selected automatically by Eviews10 software. If cointegration has been detected between series we know that there exists a long-term equilibrium relationship between them so we apply VECM in order to evaluate the short run properties of the cointegrated series.But if do not happen so ,we will use unrestricted VAR. 
\section[{Wald Test -Diagnostic Tests}]{Wald Test -Diagnostic Tests}\par
To check whether an independent variable is significant or not to explain dependent variable. 
\section[{i. The Unit Root Test}]{i. The Unit Root Test}\par
The Once a unit root has been definite for a data series, the next step is to scrutinize whether there exists a long-run equilibrium relationship or association ship among variables. The presence of long-run equilibrium (stationary) relationships among economic variables is referred to in the literature as cointegration which is very significant to avoid the risk of spurious regression. The basic idea behind cointegration is that if, in the long-run, two or more series move closely together, even though the series themselves are trended, the difference between them is constant. It is possible to regard these series as defining a long-run equilibrium relationship, as the difference between them is stationary (Hall and Henry, 1989). A lack of cointegration suggests that such variables have no long-run relationship: in principal they can wander arbitrarily far away from each other  {\ref (Dickey et. al., 1991)}. We employ the VAR based on cointegration test using the methodology developed in Johansen  {\ref (1991,} {\ref 1995)}.\par
But if cointegrating equation is found, then the VECM will be run to detect casual relationship among the variables.\par
To determine the number of cointegration vectors, Johansen  {\ref (1988,} {\ref 1989}) and Johansen and Juselius (1990) suggested two statistic tests, viz., the trace test statistic, and the maximum eigenvalue test statistic. 
\section[{Trace Test Statistics}]{Trace Test Statistics}\par
The trace test statistic can be specified as:\par
In the trace test, the null hypothesis assumes that the number of distinct cointegrating vector(s) be less than or equal to the number of cointegration relations (r). 
\section[{Maximum Eigenvalue Test}]{Maximum Eigenvalue Test}\par
The maximum eigenvalue test examines the null hypothesis of exactly r cointegrating relations 
\section[{iii. Vector Error Correction Model}]{iii. Vector Error Correction Model}\par
There have long run relationship between two series in a bivariate relationship if each series is integrated of the same order or have the same stochastic trend. If cointegration has been detected between series we know that there have exists a longterm equilibrium relationship between them. so we apply Unrestricted VECM in order to evaluate the short run properties of the series. Then we may precede to Wald Test under VECM environment to establish causal links among the variables. 
\section[{d) Emperical Analysis Framework}]{d) Emperical Analysis Framework}\par
Sometimes it is needed to convert the time series data into logarithm .The purpose of log-linear (L) is because of the value of some variables such as Gross Domestic Product in US Dollars, while the Real interest rates are in percentage. There was therefore the use of logarithm in the model to bring the variables to the same base since the variables were measured in different ways. Using Schwarz Information Criterion (SIC), the lag length is selected automatically by Eviews10 software. The null hypothesis of ADF and Phillips Perron test states that a variable is nonstationary and the null hypothesis of non-stationary is rejected if the calculated ADF statistics is less than the critical value. My next step is to determine whether the variables have a stable and non spurious cointegrating relationship among themselves. With a view to testing Cointegration, I have chosen the Johansen cointegration (1979) procedure., it is needed to ensure a VECM model is fitted to the time series data. In order to find this, data should have maintained two conditions. (1) The data should be non stationary at level. But they will become stationary at first difference. (2) It is well known fact that Johansen's cointegration test is very much sensitive to the lag length. So an appropriate lag structure is needed to find out. The Akaie Information Criterion (AIC), Schwarz Criterion (SC) and the Likelihood Ratio (LR) test are used to find the optimum number of lags required in the cointegration test. If there have one cointegrating relationship among the variables, then the Vector error correction model will run. Then we will precede to Wald block Test under VECM environment to establish causal links between variables.\par
V. 
\section[{Result and Description a) Descriptive Statistics}]{Result and Description a) Descriptive Statistics}\par
After incorporating log in each variables, the data conditions can be overviewed with the help of descriptive statistics. With descriptive statistics, it is possible to know about the variables regarding Mean, Median, Standard Deviation and so on. To test the stationarity, ADF and PHILLIPS PERRON are used. Where, Null hypothesis: There has a unit root or nonstationarity. And Alternative Hypothesis: There has not unit root or stationarity. According to the p value of all columns in table-2(a), at level, exceed 5\%. And the guideline is, when the p value is less than 5\%, we can reject the null hypothesis. So the null hypothesis cannot be rejected. That means, the variables are non stationary at level. But at the first difference the p values become less than 5\%, leads to stationary variables in table-2(b).In case of Phillips Perron Test, The results exactly similar to ADF test in table 3(a) and 3(b). Variables are non stationary at level .But when the all the variables are converted into first difference, then they will become stationary. 
\section[{c) Testing for Cointegration}]{c) Testing for Cointegration}\par
In order to determine whether there exists longrun equilibrium relationship or long run associationship among the variables of the study. To do so, the Johansen cointegration test was used. This test identifies the number of long-run relationship that exists among the group of integrated variables. Before test the johansen cointegration, there should have needed to assume two conditions.\par
(1) The data should be non stationary at level. But they will become stationary at first difference. (2) It is well known fact that Johansen's cointegration test is very much sensitive to the lag length. So an appropriate lag structure is needed to find out. The Akaie Information Criterion (AIC), Schwarz Criterion (SC) and the Likelihood Ratio (LR) test are used to find the optimum number of lags required in the cointegration test. 
\section[{i. Stationarity Test}]{i. Stationarity Test}\par
Our five variables are nonstationary at level. But when we covert all these five variables into first difference, then they become stationary. Meaning that, our 5 variables are integrated of same order . According to the p value of all tables (at level) are below 5\%.And the guideline is, when the p value is less than 5\%, we can reject the null hypothesis. So the null hypothesis(variable are stationary) is rejected. That means, the variables are non stationary at level. But at the first difference the p values become greater than 5\%, leads to stationary variables.\par
Variables are non stationary at level. But when the all the variables are converted into first difference, then they will become stationary. And if it is happen, then the study can incorporate Johansen Cointegration Test. 
\section[{ii. Lag selection}]{ii. Lag selection}\par
The number of lags are used to run Johansen Cointegration test and VAR are determined by VAR lag order selection criteria. According to the five criteria (LR, FPE, AIC, SC and HQ) are asking to take 1 lag by the sign (*). So, optimum lag would be 1. And the study uses this 1 lag in Johansen Cointegrated test and in the VECM. After selecting the lag the study uses Johansen Cointegrated Test to determine whether there exists long-run equilibrium relationship among the variables of the study. 
\section[{d) Testing for Cointegration}]{d) Testing for Cointegration}\par
In order to determine whether there exists longrun equilibrium relationship or long run associationship among the variables of the study. To do so, the Johansen cointegration test was used. This test identifies the number of long-run relationship that exists among the group of integrated variables. Trace test indicates there is 1 cointegration at level .05. From table-6, the trace statistic of 82.32 clearly exceed the critical value of and 69.81 at 5 percent confidence interval. hence, we reject the null hypothesis and conclude that there is 1 cointegrating relationship and therefore, there is long run relationship exists among the variables. Since the guideline is; when trace statistics is greater than critical value, we can reject the null hypothesis. The null hypothesis is no cointegration. 
\section[{Johansen Cointegration Test}]{Johansen Cointegration Test}\par
The Eigen value test also supported this claim of having long run relationship among the variables. The maximum eigen value statistics of 34.13 exceed the critical values of 33.87 at 95 percent confidence level. Thus, we reject the null hypothesis of no cointegrating relationships among the variables.\par
Since the variables are cointegrated or have long run associationship, the study can use VECM model.   {\ref -1}) and L TRD(-1) are independent variables. And c is constant. This is so happen in all five cases. To check whether L INT(-1) is significant variable or not to explain L GDP. Like this, Whether L M2(-1) is significant variable or not to explain L INT and so on. There (table-6) have no probability value (p value) to explain. To check this, needed to run Wald Test. For this, the study should have performed system equation. In the first model L GDP, C(1) is the coefficient. C(2) is the coefficient and so on. C(  {\ref 7}) is the constant. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Since 1st model end with C(7, second model will begin with C(8). Because it is system equation model. They are linked with each other. These will happen in case of all the five models.\par
The last value is C(35). So, it can estimate 35 coefficients and 35 p values. To estimate all the coefficient, the study uses system equation with the help of Ordinary Least square Metheod.\par
Among the Five models in the system equation, the study will concentrate on only D.L\textunderscore GDP. Because this is the dependent variable of this analysis.   Here, C1= Speed of adjustment toward equilibrium. Now, there are two issues the study have to discuss:\par
The guideline is, if the C(1) is negative in sign and significant, then we can say that , there is long run causality running from independent variables to dependent variable. And in practice, the coefficient of C1 with negative sign and probability value is 0.0009. Meaning that, There is long run causality from L INT, L REER, L M2, L TRD to L GDP.\par
ii. Short Run Causality C(3)=0, C(4)=0 , C(5)=0 and C(6) =0 If these coefficients are zero , there is no short run causality running from from L INT, L REER, L M2, L TRD to L GDP. How to check it? It can check through using WALD TEST.       
\section[{Limitation of the Study}]{Limitation of the Study}\par
A time constraint during conducting of the study was the major limitation of the study. The time available to finish the whole project was very limited especially with this kind of studies which involve complexity. No previous knowledge about research is another limitation of this study. The research is prepared on the secondary data, that is another limitation of this study. At the same time sample size is not enough due to lack of availability of the data. 
\section[{VII.}]{VII.} 
\section[{Conclusion}]{Conclusion}\par
This study empirically explores the present relationship among Interest Rate, Money supply, Real Exchange Rate, Trade openness and economic growth in the context of Bangladesh. The empirical evidence demonstrates that there have long run positive relationship coming from Real exchange rate, Money supply and Trade openness to economic growth, except interest rate. Besides this, there have casual short term relationship coming from Trade openness and Money supply to economic growth in Bangladesh,. 
\section[{VIII.}]{VIII.} 
\section[{Recommendations}]{Recommendations}\par
? Since M2 has significant positive impact on GDP.\par
This implies that if the money supply increases in considered that price level of goods and services will be increased in Bangladesh. That's why, for supplying money in the market Bangladesh bank should focus on price level and production condition of the economy.\par
? Interest Rate has negative long run relationship with Economic growth. So, policy makers should focus on interest rate, as possible as to the extend, lower interest rate. And make sure that not disencourage the foreign investment. ? Trade openness has long run and short run positive relationship with economic growth. So, as possible as to make the Bangladesh trade barrier free with sovereign. And making sure that the potential local traders are protected.\par
? Real exchange rate should be measurable. Because it's goodness depends on balance of payment.\par
Sometimes appreciation of the exchange rate may harm the country's economic growth, when account's payables are higher than receivables.\begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Volume 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9}\includegraphics[]{image-3.png}
\caption{\label{fig_1}9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{figure5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.6861285266457681\textwidth}P{0.16387147335423197\textwidth}}
Augmented Dickey Fuller\tabcellsep \\
Test\tabcellsep \\
And Phillips Perron Test.\tabcellsep \\
OLS Regression Model\tabcellsep \\
-Diagnostic Tests.\tabcellsep \\
Johansen Cointegration\tabcellsep Cointegration analysis helps to identify long-run economic relationships\\
Test\tabcellsep between the variables.\\
Vector Error Correction\tabcellsep \\
Model\tabcellsep \\
The model is stated as follows:\tabcellsep \\
\tabcellsep GDP=F(INTEREST,M2 ,REER, TRD)\\
\multicolumn{2}{l}{L\textunderscore GDP = ?+ ?1(L\textunderscore INT) + ?2(L\textunderscore M2) +?3(L\textunderscore REER) + ?4(L\textunderscore TRD) +?I}\\
Where:\tabcellsep \\
L=Log.\tabcellsep \\
GDP=Gross domestic Product.\tabcellsep \\
M2= Money supply.\tabcellsep \\
INT= Real Interest Rate.\tabcellsep \\
REER= Real Exchange rate.\tabcellsep \\
TRD= Trade openness.\tabcellsep \\
\multicolumn{2}{l}{?1 is the co-efficient for interest rate.}\\
\multicolumn{2}{l}{?2 is the co-efficient for Money supply.}\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.5964352720450281\textwidth}P{0.05262664165103189\textwidth}P{0.05103189493433396\textwidth}P{0.04784240150093808\textwidth}P{0.05422138836772983\textwidth}P{0.04784240150093808\textwidth}}
Descriptive\tabcellsep L GDP\tabcellsep L REER\tabcellsep L INT\tabcellsep L M2\tabcellsep L TRD\\
Mean\tabcellsep 24.85\tabcellsep 4.75\tabcellsep 1.56\tabcellsep 40.60\tabcellsep 3.39\\
Median\tabcellsep 24.71\tabcellsep 4.74\tabcellsep 1.27\tabcellsep 41.50\tabcellsep 3.35\\
Maximum\tabcellsep 26.12\tabcellsep 5.07\tabcellsep 2.45\tabcellsep 65.87\tabcellsep 3.87\\
Minimum\tabcellsep 23.91\tabcellsep 4.58\tabcellsep .027\tabcellsep 20.81\tabcellsep 2.81\\
Std. Dev.\tabcellsep 0.63\tabcellsep 0.106\tabcellsep 0.101\tabcellsep 15.72\tabcellsep 0.32\\
Skewness\tabcellsep 0.40\tabcellsep 1.186\tabcellsep 1.22\tabcellsep 0.223\tabcellsep -0.19\\
b) Testing for Sationarity\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Time-series data are often assumed to be non-}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{stationary and thus it is necessary to perform a pretest}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{to ensure there is a stationary cointegrating relationship}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{among variables in order to avoid the problem of}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{spurious regression. To do so, this study adopted the}\tabcellsep \tabcellsep \tabcellsep \\
Augmented Dickey -Fuller (ADF) test.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: a): Results of PHILLIPS PERRON Test at levelTable 3(b): Results of PHILLIPS PERRON Test with 1st difference]} 
\caption{\label{tab_4}Table 3 (}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.3519908987485779\textwidth}P{0.08026166097838452\textwidth}P{0.15568828213879407\textwidth}P{0.06575654152445962\textwidth}P{0.10346985210466439\textwidth}P{0.09283276450511946\textwidth}}
\multicolumn{5}{l}{Economic Growth and Macroeconomic Fundamentals: Evidence from Bangladesh}\tabcellsep \\
L TRD Stationarity Test\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Variables\tabcellsep AT level\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{At First Difference}\tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2019\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 17\\
Variables L\textunderscore GDP L\textunderscore INT L\textunderscore M2 L\textunderscore REER L\textunderscore TRD\tabcellsep Test Statistics -2.37 ----1.85\tabcellsep PHILLIPS PERRON TEST 1\% 5\% -3.68 --3.68 --3.68 --3.67 -2.96 -3.67 -2.96\tabcellsep 10\% -2.63 -2.63 -2.63 -2.63 -2.63\tabcellsep P-value 0.1568 0.3889 0.7343 0.9304 0.3486\tabcellsep Volume XIX Issue II Version I\\
Variables D. L\textunderscore GDP D. L\textunderscore INT D. L\textunderscore M2 D.L\textunderscore REER D.L\textunderscore TRD\tabcellsep Test Statistics -9.87 -5.13 -6.56 -3.63 -5.27\tabcellsep PHILLIPS PERRON TEST 1\% 5\% -3.67 -2.96 -3.67 -2.96 -3.67 -2.96 -3.67 -2.96 -3.67 -2.96\tabcellsep 10\% -2.63 -2.63 -2.63 -2.63 -2.63\tabcellsep P-value 0.0000 0.0003 0.0009 0.01123 0.0002\tabcellsep ( ) C Global Journal of Management and Business Research\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep © 2019 Global Journals\tabcellsep \end{longtable} \par
  {\small\itshape [Note: To test the stationarity, correlogram is used. Where, Null hypothesis: Variable is stationary. And Alternative Hypothesis: Variable is not stationary.]} 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2019\\
( )\end{longtable} \par
  {\small\itshape [Note: Null: Not Optimum lag.C]} 
\caption{\label{tab_6}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.731118881118881\textwidth}P{0.007925407925407925\textwidth}P{0.007925407925407925\textwidth}P{0.057459207459207455\textwidth}P{0.005944055944055944\textwidth}P{0.003962703962703962\textwidth}P{0.03566433566433567\textwidth}}
Lag\tabcellsep LogL\tabcellsep LR\tabcellsep FPE\tabcellsep AIC\tabcellsep SC\tabcellsep HQ\\
\multicolumn{2}{l}{0 -4806.030}\tabcellsep NA\tabcellsep \multicolumn{3}{l}{2.90e+10 43.95461 44.06294}\tabcellsep 43.99836\\
\multicolumn{7}{l}{1 -4751.646 104.7944 2.76e+10** 43.90544** 44.77206** 44.25544**}\\
\multicolumn{6}{l}{2 -4682.387 129.0310 2.30e+10 43.72043 45.34533}\tabcellsep 44.37668\\
\multicolumn{5}{l}{* indicates lag order selected by the criterion}\tabcellsep \tabcellsep \\
\multicolumn{6}{l}{LR: sequential modified LR test statistic (each test at 5\% level)}\tabcellsep \\
\multicolumn{3}{l}{FPE: Final prediction error}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{AIC: Akaike information criterion}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{SC: Schwarz information criterion}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{HQ: Hannan-Quinn information criterion}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.37242268041237114\textwidth}P{0.05257731958762887\textwidth}P{0.054768041237113395\textwidth}P{0.1577319587628866\textwidth}P{0.06791237113402061\textwidth}P{0.08981958762886598\textwidth}P{0.054768041237113395\textwidth}}
\multicolumn{3}{l}{No. of CE(s) Trace Statistics Max Eigen Statistics}\tabcellsep \multicolumn{4}{l}{Critical Values (5\%) Critical Values (5\%)}\\
\tabcellsep \tabcellsep \tabcellsep Trace\tabcellsep P-value\tabcellsep \multicolumn{2}{l}{Max-Eigen P-Value}\\
None***\tabcellsep 82.32\tabcellsep 34.93\tabcellsep 69.81\tabcellsep .0036\tabcellsep 33.87\tabcellsep .0373\\
At most 1\tabcellsep 47.38\tabcellsep 26.23\tabcellsep 47.85\tabcellsep 0.055\tabcellsep 27.58\tabcellsep .0737\\
At most 2\tabcellsep 21.15\tabcellsep 13.69\tabcellsep 29.79\tabcellsep .347\tabcellsep 21.13\tabcellsep .3902\\
At most 3\tabcellsep 7.45\tabcellsep 7.44\tabcellsep 15.49\tabcellsep .5249\tabcellsep 14.26\tabcellsep .4384\\
At most 4\tabcellsep .0179\tabcellsep 0.0179\tabcellsep 3.84\tabcellsep .8933\tabcellsep 3.84\tabcellsep .8933\\
\multicolumn{5}{l}{Trace Test and Max Eigen Test indicates 1 co-integrating equ(s) at .05 level.}\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: ***Denotes rejection of the hypothesis at the .05 level.]} 
\caption{\label{tab_8}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2019\\
22\\
Volume XIX Issue II Version I\\
( )\\
Global Journal of Management and Business Research\\
© 2019 Global Journals 1\end{longtable} \par
  {\small\itshape [Note: C]} 
\caption{\label{tab_9}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{P{0.6499999999999999\textwidth}P{0.19999999999999998\textwidth}}
\multicolumn{2}{l}{(a): Results of Wald Test}\\
\tabcellsep WALD TEST\\
\tabcellsep Null Hypothesis: C(3)=0\\
Test Statistics\tabcellsep Probability\\
Chi-square\tabcellsep .8896\\
\multicolumn{2}{l}{Null: L INT(-1) can not cause L GDP.}\\
\multicolumn{2}{l}{Alt: L INT(-1) can cause L GDP.}\\
In table-8,\tabcellsep \\
L INT(-1)= C(3) coefficient.\tabcellsep \end{longtable} \par
 
\caption{\label{tab_11}Table 10}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{P{0.658450704225352\textwidth}P{0.19154929577464788\textwidth}}
\multicolumn{2}{l}{b): Results of Wald Test}\\
\tabcellsep WALD TEST\\
\tabcellsep Null Hypothesis: C(4)=0\\
Test Statistics\tabcellsep Probability\\
Chi-square\tabcellsep .2875\\
Here,\tabcellsep \\
\multicolumn{2}{l}{Null: L REER (-1) can not cause L GDP.}\\
\multicolumn{2}{l}{Alt: L REER (-1) can cause L GDP.}\\
In table-8,\tabcellsep \\
L REER(-1)= C(4) coefficient.\tabcellsep \end{longtable} \par
 
\caption{\label{tab_12}Table 10 (}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{P{0.6509756097560976\textwidth}P{0.19902439024390245\textwidth}}
\multicolumn{2}{l}{c): Results of Wald Test}\\
\tabcellsep WALD TEST\\
\tabcellsep Null Hypothesis: C(5)=0\\
Test Statistics\tabcellsep Probability\\
Chi-square\tabcellsep .0289\\
Here,\tabcellsep \\
\multicolumn{2}{l}{Null: L M2(-1) can not cause L GDP.}\\
Alt: L M2(-1) can cause L GDP.\tabcellsep \\
In table-8,\tabcellsep \\
L M2(-1)= C(5) coefficient.\tabcellsep \end{longtable} \par
 
\caption{\label{tab_13}Table 10 (}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.85\textwidth}}
Dependent Variable: L GDP\end{longtable} \par
 
\caption{\label{tab_14}Table 11 (}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12} \par 
\begin{longtable}{P{0.85\textwidth}}
L\textunderscore GDP=15.1069-0.0085L\textunderscore INT+1.333L\textunderscore REER+0.0221L\textunderscore M2+0.7567L\textunderscore TRD\\
In this regression model, R 2 =.980, This is\\
desirable. More than 60\% of the R-squared value\\
indicates the model is nicely fitted. That means, this\\
regression model is nicely fitted with .996 R-Squared\\
value. Besides this, probability value of F-statistics is\\
less than 5\%, which implies L INT, L REER, L M2 and\\
LTRD are jointly good to explain the dependent variable.\\
Through diagnostic checking, it is known that this\\
regression model is beyond the existence of serial\\
correlation and Heteroscedasticity. And the residuals\\
are normally distributed. In short, all good attributes\\
have in this model. In (table-), there are four\\
independent variables. These are L INT, L REER, L M2\\
and L TRD.\end{longtable} \par
 
\caption{\label{tab_15}Table 12 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13} \par 
\begin{longtable}{P{0.85\textwidth}}
VI.\end{longtable} \par
 
\caption{\label{tab_16}Table 13 (}\end{figure}
 			\footnote{© 2019 Global Journals} 		 		\backmatter  			 \par
Here, Null: L TRD(-1) can not cause L GDP. Alt: L TRD(-1) can cause L GDP.\par
In table-8 , L TRD(-1)= C(6) coefficient.\par
According to p value .034, we can reject the null. That means, C(6) in table-8 , can affect dependent variable L GDP. There is short run causality from L TRD L GDP. 
\subsection[{Summary of Wald Test}]{Summary of Wald Test}\par
There is short run causality running from Money supply and Trade openness to Economic Growth. And There is no short run causality running from Interest rate and Exchange Rate to Economic Growth. 
\subsection[{f) Diagonistic Tests i. Test of Heteroskedasticity}]{f) Diagonistic Tests i. Test of Heteroskedasticity}\par
According to the classical linear regression model (CLRM) assumption, the residuals should be homoscedastic in nature. Here, Null Hypothesis: There is homoscedasticity. And alternative hypothesis: There is no homoscedasticity. Since the Prob.chi-square is more than 5\%. That is why, the null hypothesis cannot be rejected. So there is no Heteroskedasticity. This is desirable. Here, the null hypothesis: there is no serial correlation in the residuals. And Alternative hypothesis: There is serial correlation in the residuals. 
\subsection[{ii. Serial correlation LM Test}]{ii. Serial correlation LM Test}\par
Since the probability value of chi-square .3914, which is less than 5\%. So, we cannot reject the null hypothesis, leads to there is no serial correlation. This is desirable. 
\subsection[{iii. Normality Test}]{iii. Normality Test}\par
Residuals should be normally distributed. To test the normality of the residuals, the study uses Histogram Normality Test. In histogram test, The Jarque-Bera Probability is more than 5\%. That means, we accept the Null hypothesis, Residuals are normally distributed. This is desirable. Here, the null hypothesis: there is no serial correlation in the residuals. And Alternative hypothesis: There is serial correlation in the residuals. Since the probability value of chi -square .0231, which is less than 5\%. So, we can reject the null hypothesis, leads to there is serial correlation. The existence of serial correlation in the model is not desirable. Now we have to remove the serial correlation from the model. This is possible through creating a variable that is one period lag of the dependent variable(L\textunderscore GDP). The new variable become Lag\textunderscore L\textunderscore GDP. That means, Lag\textunderscore L GDP=L\textunderscore GDP(-1). 
\subsection[{iii. Normality Test}]{iii. Normality Test}\par
Residuals should be normally distributed. To test the normality of the residuals, the study uses Histogram Normality Test. In histogram test, The Jarque-Bera Probability is more than 5\%. That means, we accept the Null hypothesis, Residuals are normally distributed. This is desirable.			 			  				\begin{bibitemlist}{1}
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