Slide 1

INTERPRETATION OF RESULTS OF REGRESSION ANALYSISRegression

Cont..R2= 0.963456, which is near to 1, this shows that our model is best fitted.CB (Central Bank) is insignificant because the value of t-Statistics is lies between 1.96 which is 0.34642 & P- value is greater than 0.05 which is 0.7309 , so we accept the Ho (Null hypothesis) which is insignificant.NFA (Net Foreign Assets) is significant because the value of t-Statistics is lies beyond the 1.96, which is 10.15263 & P value is less than 0.05 which is 0.0000 so we accept the H1 which is significant.

Multicollinearity1. Simple Correlation Co-efficient

Table shows that the both independent variables (Central Bank and Net Foreign Assets) are highly correlated with each other. As rxy=0.943594 which is nearest to 1, This shows that there is Multicolinearity but it is Imperfect Multicolinearity.

CBNFACB1.0000000.943594NFA0.9435941.000000Cont..2. High R2 with Insignificant Co-efficient

Cont..The value of R2= 0.963456, which shows the high value nearest to 1, but CB (Central Bank) is insignificant because the value of t-Statistics lies between 1.96 which is 0.34642 & P- value is greater than 0.05 which is 0.7309, its means Multicolinearity is exist between the variables.

Cont..3. Variance Inflating Factor (VIF)VIF= 1/1-R2 VIF= 1/1-0.963456 = 27.36The decision criteria for VIF is if the value of R2 is high, the value of VIF is also high, A rule of thumb is if VIF > 10 there is Multicollinearity, here R2= 0.963456 nearest to 1 and VIF=27.37 greater than 10 which means there is multicolinearity.

Cont4. Tolerance (ToL)ToL= 1- R2 =1/VIFToL= 1/27.36 = 0.036549ToL= 1- 0.963456= 0.036544The decision criteria for ToL is if the value of R2 is high, the value of ToL will be lower, A rule of thumb is if ToL < 0.10 there is Multicollinearity & Vice Versa, here R2= 0.963456 nearest to 1 and ToL=0.03654 less than 0.10, which means there is multicolinearity.

Heteroskedasticity1. Graphical Method

Graph Shows unequal spread above and below the regression line, which shows that the variance of the error term is not constant over time. So there is heteroskedasticity.

Cont..2. Numerical Method:ARCH Method

Cont..F-test & P-value are the decision criteria for heteroskadesticity. Higher the F-test value , lower will be the P-value and result goes towards significant level i.e p-value will be less than 0.05. The value of F-test here is lower than 3 which is the criteria of significant for F test, which is F=1.035158 & P-value is greater than the significant level (0.05) which is P=0.3154. Its means that we accept the Ho (There is no heteroskedasticity).

Cont..White Criteria

Cont..The value of F-test here is lower than 3, which is the criteria of significant for F test, which is F=0.540308 & the value of P is greater than the significant level i.e 0.05, which is P= 0.7444, so we accept the null hypothesis Ho i.e. there is no heteroskedasticity.AutocorrelationGraphical Method

Graph shows the unequal trend means there is autocorrelation.

Cont..2. Formal MethodDurbin Watson Test

Cont..If the value of Durbin-Watson Stats is between1.75 to 2.25, then there is no autocorrelation. In our results the value of Durbin-Watson Stats is 1.158370, this is out of the range so we conclude that there is autocorrelation means the disturbance term depend on its previous value.Cont..Breusch Godfrey Test

Cont..In Breusch-Godfrey Serial Correlation LM Test the decision criteria depends on the values of P and F-Stats. If the value of P>0.05 then we accept the Ho, which is there is no autocorrelation and vice versa.The value of in result is P=0.0002, which is less then the Significant level (0.05). so we accept the H1 hypothesis which is there is Autocorrelation.

Cont..Resolving the AutocorrelationAR(1)

Cont..AR(2)

The value of Durbin Watson stat=1.8406, which is in range of 1.75 to 2.25. so now there is no autocorrelation after Second order.

Cont..AR(3)

Dummy VariableIntercept Dummy

Cont..Decision is made on the basis of t-stat & p-value. If values are in significant range there is effect of dummy & vice versa.Here t-stat=1.20125 between -1.96 to +1.96 and p-value=0.2373 greater than 0.05, so the variable is not effected by the dummy variable.Cont..Slope Dummy

Cont..For cb*D1 t-stat= -2.217112 beyond -1.96 to +1.96 and p-value=0.0366 less than 0.05, so the variable is effected by the dummy variableFor nfa*D1 t-stat=2.93757 beyond -1.96 to +1.96 and p-value=0.0057 less than 0.05, so the variable is effected by the dummy variableIn both the dummy coefficient will be added to the coefficient of respective variable. Y = + (1+ 3) X1 + (1+ 4)X2 + t Y =81191.32 + (0.261723 + (-0.526681) )X1 + (0.828891 +0.561483) X2 Y =81191.32 -0.264958X1 + 1.390374X2Cont..Combined Dummy

Cont..For D1 t-stat= 0.163645 between -1.96 to +1.96 and p-value=0.0871 less than 0.05, so the is not effected by the dummy variableFor cb*D1 t-stat= -2.143855 beyond -1.96 to +1.96 and p-value=0.0391 less than 0.05, so the variable is effected by the dummy variableFor nfa*D1 t-stat=2.772699 beyond -1.96 to +1.96 and p-value=0.0088 less than 0.05, so the variable is effected by the dummy variable The slope dummy coefficient will be added to the coefficient of respective variable .Y = + (1+ 4) X1 + (1+ 5)X2 + tY =73967.29+ (0.268740 + (-0.532227) )X1 + (0.833246 +0.553606) X2Y =81191.32 - 0.263487X1 + 1.386852X2

More Than One DummyIntercept

For Both Dummy t-stat and p-value are insignificant, so have no effect.

Cont..Slope

Cont..The t-stat & p-value for the slope dummy 2(D2) is insignificant, so the variables are not effected by D2. cb*D1 t-stat=-2.166308 and p-value=0.0374 , nfa*D1 t-stat=2.654541 and p-value=0.0120 which are significant, so effect the coefficient of variables. The slope dummy coefficient of D1 will be added to the coefficient of respective variable . Y = + (1+ 3) X1 + (1+ 4)X2 + t Y =72256.14+ (0.212060 + (-0.589406) )X1 + (0.855205 +0.612761) X2 Y =81191.32 - 0.377346X1 + 1.1.467966X2

Cont..Combined

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