Dynamic Regression A Simulation Exercise Math Problem

Dynamic Regression A Simulation Exercise - Math Problem ExampleFrom the chart it is withal the hurtle is also evident in the grocery and locomote returns and this shows that a drop in the market returns will also signify a drop in the returns of the stocks in the market. Finally from the chart it is evident that there was a decline in the market returns in 1987 showing that returns for the other stocks also declined.We ingestion 120 0bservations to estimate the personate estimate the set rjt = j + jrmt + Ujt for both stocks, we use MOTOR return data for the year 1976 to 1985, after estimation render the TSM software the results show that rjt = 0.00255 + 0.7193 rmtthe above model means that is we hold all factors constant and the market return take is equal to zero then the MOTOR stock return will be 0.00255, also if we hold all factors constant and we increase the market return level by one building block then the MOTOR stock return level will increase by 0.7193 units. ... The above model means that is we hold all factors constant and the market return level is equal to zero then the GPU stock return will be 0.00063, also if we hold all factors constant and we increase the market return level by one unit then the GPU stock return level will increase by 0.4297 units. The R squared for this model is 0.0854and this means that 8.54% of deviations in the dependent variable are explained by the independent variable. The correlation of determination R squared value for this model depicts a infirm relationship between the explanatory variable and the dependent variable.Hypothesis testingWe test opening for the estimated coefficients in the two models,MOTOR modelrjt = 0.00255 + 0.7193 rmtMOTOR model ConstantNull hypothesis = 0Alternative hypothesis 0Standard error 0.00737 Coefficient 0.00255 T calculated = 0.00255 / 0.00737 = 0.34599T critical at 95% level of test = 1.95996When the T calculated value is less than the T critical value we accept the null hypo thesis, in the above case indeed we accept the null hypothesis that = 0 and therefore the constant is not statistically significant at 95% level of test.Motor Model angleNull hypothesis = 1Alternative hypothesis 1Standard error 0.12481 Coefficient 0.7193T calculated = 1- 0.7193/ 0.12481= 2.249T critical at 95% level of test = 1.95996When the T calculated value is greater than the T critical value we disavow the null hypothesis, in the above case therefore we reject the null hypothesis that = 0 and therefore the constant is statistically significant at 95% level of test.GPU modelrjt = 0.00063 + 0.4297 rmtGPU model ConstantNull hypothesis = 0Alternative hypothesis 0Standard error 0.00841 Coefficient 0.00063