Global Journal of Management and Business Research, C: Finance, Volume 19 Issue 6

We see that the adjusted R-square value of three dependent variables are better representative of combined effect of independent variables. But how much an independent variable is significant to explain the dependent variable is shown by ordinary least square method using proc equations as follows. LREER = C(1)*LREER(-1) + C(2)*LREMIT(-1) + C(3)*LGDPG(-1) + (4)*LINTTRADE(-1) + C(5)---------------------------------------- (6) LREMIT = C(6)*LREER(-1) + C(7)*LREMIT(-1) + C(8)*LGDPG(-1) + C(9)*LINTTRADE(-1) + C(10)----------------------------------- (7) LGDPG = C(11)*LREER(-1) + C(12)*LREMIT(-1) + C(13)*LGDPG(-1) + C(14)*LINTTRADE(-1) + C(15)-----------------------------(8) LINTTRADE = C(16)*LREER(-1) + C(17)*LREMIT(-1) + C(18)*LGDPG(-1) + C(19)*LINTTRADE(-1) + C(20)------------------------(9) Here, the proc makes four equations that includes 20 coefficients. The t-statistics is the result of coefficient divided by standard error. Now after estimating the OLS (ordinary least squares) method we get the probability (P) value. The decision can be given using P value. When the p value is more than 5 %, the particular independent variable is not significant to explain the dependent variable. Coefficient Std. Error t-Statistic Prob. C(1) 1.009403 0.097056 10.40024 0.0000 C(2) 0.004419 0.049923 0.088519 0.9296 C(3) -0.019447 0.060057 -0.323806 0.7468 C(4) 0.072215 0.093604 0.771496 0.4422 C(5) -0.255211 0.439128 -0.581176 0.5624 C(6) -0.686565 0.175071 -3.921634 0.0002 C(7) 0.794698 0.090052 8.824867 0.0000 C(8) 0.080309 0.108332 0.741318 0.4602 C(9) 0.222218 0.168844 1.316114 0.1911 C(10) 2.727361 0.792108 3.443170 0.0008 C(11) 0.224090 0.372866 0.600993 0.5492 C(12) 0.076991 0.191793 0.401426 0.6890 C(13) 0.173932 0.230726 0.753847 0.4527 C(14) 0.344923 0.359605 0.959172 0.3398 C(15) -1.000043 1.687031 -0.592783 0.5547 C(16) -0.239511 0.153398 -1.561366 0.1216 C(17) 0.115976 0.078904 1.469834 0.1447 C(18) 0.098354 0.094921 1.036161 0.3026 C(19) 0.681986 0.147942 4.609807 0.0000 C(20) 1.900599 0.694050 2.738420 0.0073 The probability value helps to see which independent variable is significant to explain the dependent variable. The decision is to be taken at 5 % significance level. If the p value is greater than 5% than null hypothesis is rejected that is there is no significant relationship between independent and dependent variable with their respective coefficients. Here, C (1), C (6), C (7), C (10), C (19) and C (20) is significant at 5% significant level where the P value is less than 5% in the following four equations above. h) Wald Test statistics Wald test statistics helps to identify whether there are any short run causality or not. The null hypothesis explains there is no short run causality and alternative hypothesis favors the short run relationship. P value determines accept or reject null hypothesis. When it cross 5%, Null hypothesis is accepted and there is no short run causality. © 2019 Global Journals 21 Global Journal of Management and Business Research Volume XIX Issue VI Version I Year 2019 ( ) C Factors Influencing Exchange Rate: An Empirical Evidence from Bangladesh