1. In a regression model, WAGE (weakly wage in dollars) was regressed on the ind
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Question
1. In a regression model, WAGE (weakly wage in dollars) was regressed on the independent variables EDUC (education in years), IQ (IQ test score), KWW (KWW test score), EXPER (work experience in years), and AGE (age in years). The following result was obtained (estimated standard errors of coefficients are in parentheses):
WAGE = -682.5 + 46.1 EDUC + 4.0 IQ + 7.8 KWW + 11.4 EXPER + 6.2 AGE. (7.4) (1.0) (2.0) (3.7) (5.1)
Using these results, do the following.
(a) Given the other factors, does age have a statistically significant impact on wage? Explain why or why not.
(b) Carefully interpret the regression results. Be specific about each coefficient.
(c) Test the hypothesis that one additional year of education leads to an increase in weakly wage by $50.
(d) Suppose EXPER is excluded from this regression. What do you predict will happen to the coefficient on AGE and why?
2. Use Stata or any other software to estimate the model in Problem 1 using dataset wage2. Provide regression output. Make sure you obtain the same results.
3. (a) Use dataset wage2 to estimate the following model:
WAGE = b0 + b1 EDUC + b2 EXPER + u. Provide regression output and interpret the results.
(b) Consider a model where EXPER is omitted:
WAGE = sigma0 + sigma1 EDUC + v. Estimate this model, provide regression output and interpret results.
(c) Calculate the omitted variable bias in the coefficient on EDUC associated with omitting EXPER.
(d) Run a regression of EXPER on EDUC, provide output, and interpret the results. Verify that the omitted variable bias you found in part (c) is equal to the product of the estimate of b2 from part (a) and the estimate of the slope in the regression of EXPER on EDUC.
Explanation / Answer
Specific softwares are needed for this programme viz, stata, advanced excel, e-view, spss and so on. Unless those tools are presents analysis cannot be done.
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