Methodology: The Economic model: The aim of our paper is to find out the contribution of stock market capitalization and investment to the growth rate of GDP. Since all our variables are continuous variables, and our model is linear, we are using OLS method. GDP growth rate = β0+β1Stockmarket Capitalization+ β2Investment to purchasing power parity ratio + ε This equation shows the model that we are going to estimate. We know the stock market and investment sector in Bangladesh is not quite developed. We want to find out regardless of this fact how much these sectors are contributing to the growth of this country. Data collection and variables: The data we have collected is secondary data from FRED (Federal Reserve Economic Data). Our dependent variable is GDP growth rate and independent variables are Investment and Stock market capitalization. We could not collect many observations since Bangladesh Economy was not very good before 1990s. Here are some details about the variables, Variable Obs Mean InvestPPP gdpgr SMCGDP 52 58 25 12.31159 4.271062 11.51529 Std. Dev. 6.745164 3.779178 9.804513 Min Max 1.765136 -13.97373 1.38023 23.80312 10.95279 34.3251 Table 1: Brief detail of the variables. In the table we can see that InvestPPP is the data of investment of Bangladesh as a share of the purchasing power parity. We used this data because it was the best fit for our model. All other investment data had some shortcomings for example: the investment sector consists of financial and non-financial investments, of which financial investments are not very prominent in Bangladesh. So, until recently the investment in financial sector was not significant. So, rather than taking bits of data from many sources we chose this data. And we can see the highest share of investment in PPP is 23.8% whereas mean is 12.31%. The other two variables are SMCGDP which is for stock market capitalization which has a mean of 11.52 and gdpgr represents GDP growth rate which has a mean of 4.27%, 10.95% being the highest and -13.97% being the lowest (due to liberation war our economy plunged) Findings: This following table is our regression output. reg gdpgr SMCGDP InvestPPP Source SS df MS Model Residual 7.40024186 6.13981919 2 15 3.70012093 .409321279 Total 13.5400611 17 .796474179 gdpgr Coef. SMCGDP InvestPPP _cons -.0373804 .2118276 1.375186 Std. Err. .0264398 .0498815 .9230485 t -1.41 4.25 1.49 Number of obs F(2, 15) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.178 0.001 0.157 = = = = = = 18 9.04 0.0027 0.5465 0.4861 .63978 [95% Conf. Interval] -.0937356 .1055077 -.5922454 .0189748 .3181474 3.342617 Table 2: Regression output. From the table we can see the value of F statistics is 9.04 which is basically the ratio of MSM and MSR. The p value is 0.0027 which is less than our alpha value 0.05 that means our independent variables are reliable for predicting the dependent variable. The value of R squared is 0.5465 that means around 54.65% of the variance in the dependent variable which is GDP growth rate can be explained by our independent variables which are investment and stock market capitalization. The value of our β0, β1, β2 is 1.375186, -0.0373804 and 0.02118276. We can see our one variable which is investment is significant with a standard error of 0.0498815. And stock market capitalization is statistically insignificant with a standard error of 0.0264398. Now we will do a few tests to see whether the results we got have any heteroskedasticity, autocollinearity, multicollinearity or not. 1. Heteroskedasticity: Graph Plot for heteroskedasticity: .2 .1 0 Residuals -.1 -.2 -.3 1.4 1.5 1.6 Fitted values 1.7 1.8 Fig 1: Check for heteroskedasticity. On the y axis we have residuals. We don’t see any cluster of data so data is homoscedastic as per this graphical test. Breusch-Pagan test: . hettest , rhs fstat Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: loginv logsmc F(2 , 15) Prob > F = = 0.10 0.9013 Table 3: Breusch-Pagan test for heteroskedasticity. The null hypothesis of OLS is constant variance. So here the null hypothesis is constant variance that means the variance is constant for all data. We can see P value>0.05 so we cannot reject the null. Hence data is homoscedastic. 2. Auto collinearity: 0 -.1 -.2 -.3 Residuals .1 .2 We generated residuals using Stata for testing auto-collinearity. In the diagram we plotted residuals over the year keeping yline at 0. 1990 1995 2000 year 2005 2010 Fig 2: Plotting residuals. We can see a little positive autocorrelation here, as the error terms are following the pattern of previous year. For being absolutely sure we will do another test which is Durbin-Watson test. Durbin-Watson test: . estat dwatson Durbin-Watson d-statistic( 3, 18) = 1.227876 Table 4: Durbin-Watson test. We can see that the value of d is close to 2 so the value of rho ρ would be almost equal to 0, means a positive autocorrelation. Solution for auto-correlation: White-het robust variance: We do this for eliminating any kind of auto-collinearity and heteroskedasticity in data. We do not have heteroskedasticity but we have auto-collinearity. . newey gdpgr SMCGDP InvestPPP, lag(2) Regression with Newey-West standard errors maximum lag: 2 gdpgr Coef. SMCGDP InvestPPP _cons -.0373804 .2118276 1.375186 Newey-West Std. Err. .0201369 .0563276 .9531284 Number of obs = F( 2, 15) = Prob > F = t P>|t| -1.86 3.76 1.44 0.083 0.002 0.170 18 7.17 0.0065 [95% Conf. Interval] -.0803013 .0917682 -.6563593 .0055404 .331887 3.406731 Table 5: White -het robust. We can see after using white het robust the p value for stock market capitalization is 0.082 whereas in tabe-1 it was 0.178. That means the insignificance f this variable has lessened. 3. Multicollinearity: . vif . corr SMCGDP InvestPPP gdpgr (obs=18) SMCGDP Invest~P SMCGDP InvestPPP gdpgr 1.0000 0.3794 0.0371 1.0000 0.6972 Variable VIF 1/VIF InvestPPP SMCGDP 1.17 1.17 0.856048 0.856048 Mean VIF 1.17 gdpgr 1.0000 Table 6: Test for Multi-collinearity. VIF is variance inflation factor. We can see the value of VIF is less than 10, that means there is no multicollinearity. Also, the values are correlation between the values is very less, there is no multicollinearity in the data. That means our independent variables are not related to each other. They do not have any significant impact on the other one.
