Using the 526 observations on workers in a certain dataset, we include educ (years of education),...

 

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Using the 526 observations on workers in a certain dataset, we include educ (years of education), exper (years of labor market experience) and tenure (years with the current employer) in regression explaining log(wage) (logarithm of workers' average hourly earnings): In(wage) = Po + B₁educ + B₂exper+B3tenure + u₁. (1) Part of the EVIEWS estimation output is given below: Dependent Variable: LOG(WAGE) Method: Least Squares Date: 10/16/14 Time: 10:32 Sample: 1 526 Included observations: 526 Variable с EDUC EXPER TENURE R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Table 2 Coefficient Std. Error 0.284360 0.092029 0.004121 0.022067 t-Statistic 0.104190 2.729230 X 0.007330 0.001723 2.391437 0.003094 7.133071 Mean dependent var Akaike info criterion 0.316013 0.312082 S.D. dependent var 0.440862 101.4556 Schwarz criterion -313.5478 Hannan-Quinn criter. 80.39092 Durbin-Watson stat 0.000000 Prob. 0.0066 0.0000 0.0171 0.0000 1.623268 0.531538 1.207406 1.239842 1.220106 1.768805 (a) Use the information provided in Table 2 to test the hypotheses that each of the coefficients is (individually) significantly different to zero at the 1% level. Use a 2- sided alternative for the intercept and 1-sided alternatives for the regressors' coefficients. (12 marks) (b) Provide interpretation of the coefficients of model where possible. (5 marks) (c) Using information from Table 2 test the overall significance of the model at the 5% significance level. Would your answer change if the significance changes to 10%? level of the test (6 marks) Using the 526 observations on workers in a certain dataset, we include educ (years of education), exper (years of labor market experience) and tenure (years with the current employer) in regression explaining log(wage) (logarithm of workers' average hourly earnings): In(wage) = Po + B₁educ + B₂exper+B3tenure + u₁. (1) Part of the EVIEWS estimation output is given below: Dependent Variable: LOG(WAGE) Method: Least Squares Date: 10/16/14 Time: 10:32 Sample: 1 526 Included observations: 526 Variable с EDUC EXPER TENURE R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Table 2 Coefficient Std. Error 0.284360 0.092029 0.004121 0.022067 t-Statistic 0.104190 2.729230 X 0.007330 0.001723 2.391437 0.003094 7.133071 Mean dependent var Akaike info criterion 0.316013 0.312082 S.D. dependent var 0.440862 101.4556 Schwarz criterion -313.5478 Hannan-Quinn criter. 80.39092 Durbin-Watson stat 0.000000 Prob. 0.0066 0.0000 0.0171 0.0000 1.623268 0.531538 1.207406 1.239842 1.220106 1.768805 (a) Use the information provided in Table 2 to test the hypotheses that each of the coefficients is (individually) significantly different to zero at the 1% level. Use a 2- sided alternative for the intercept and 1-sided alternatives for the regressors' coefficients. (12 marks) (b) Provide interpretation of the coefficients of model where possible. (5 marks) (c) Using information from Table 2 test the overall significance of the model at the 5% significance level. Would your answer change if the significance changes to 10%? level of the test (6 marks)

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The detailed answer for the above question is provided below Answer a Answer The intercept as well as the independent variables EDUCATION and TENURE a... View the full answer