ECMT1020 Introduction to Econometrics Report Writing – Statistics Assignment Help

Assignment Task

Task

Questions:

1. Fit a wage equation by regressing EARNINGS on EXP, and perform t tests on the intercept and slope coefficient. Then, perform an F test on the explanatory power of the model and explain the relationship between this F test and the t test on the slope coefficient.

2. Fit another wage equation by regressing EARNINGS on S and EXP, and interpret all the parameter estimates (with attention paid to their significance). Compare the slope coefficient of EXP in this equation with the one obtained in Question 1 and interpret the difference.

3. Perform an F test of the explanatory power of the equation you obtained in Question 2. Calculate the F statistic using R2 of the fitted regression and verify it is the same as the F statistic in your Stata output.

4. Regress the logarithm of EARNINGS on S and EXP. Carefully interpret the regression results, perform t tests on the coefficients and F test of the explanatory
power of the model.

5. Use the Box and Cox procedure (Steps 1–3) described on p. 211 of the textbook to evaluate whether the dependent variable of a wage regression of EARNINGS on S and EXP should be linear (like in Question 2) or logarithmic (like in Question 4).

6. Regress S on ASVABC, MALE, SM, SF, ETHHISP and ETHBLACK. Use your results to answer the question: Does ethnicity affect educational attainment, and if yes, how?

7. Redo Question 6 making ETHBLACK the reference category. What are the impacts of change of reference on the interpretation of the coefficients and the statistical tests (t tests of the coefficients and F test of the model)?

8. Define a slope dummy variable as the product of MALE and ASVABC. Regress the S on ASVABC, MALE, SM, SF, ETHHISP, ETHBLACK, and the slope dummy variable. First, explain what this regression with slope dummy variable can be useful for. Second, what are your findings based on your regression results?

9. Fit a wage equation by regressing the logarithm of EARNINGS on S, EXP and MALE. Perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension. Why heteroskedasticity is a concern when we conduct the regression analysis?

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