You estimate the following regression: In(w) = b + b Educ+b Female +b(Female Educ) +...

 

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You estimate the following regression: In(w) = b + b Educ+b Female +b(Female × Educ) + b_Exper+ b Exper², where w is average hourly earnings, Female is a female dummy variable, Educ is years of education, Exper is potential experience (number of years since completion of schooling). You estimate the regression on a large sample of data and find that all coefficients are statistically significant at the 1% level. (a) Consider a male with 18 years of education and 3 years of experience. Express the predicted change in In(w) associated with an additional year of experience as a function of the estimated regression coefficients. (b) Let 4ln(w) denote the predicted change you estimated in (a). A 95% confidence interval for Aln(w) would equal Aln(w) ± 1.96× se (Aln(w)). Rearrange the regression so that se (Aln(w)) is the standard error of an estimated coefficient in the transformed regression. (c) Consider a male with 18 years of education and 4 years of experience. Express the predicted change in In(w) associated with an additional year of experience as a function of the estimated regression coefficients. (d) Is the difference between your answers to (c) and (a) statistically significant at the 5% level? Why or why not? (e) Would your answers to (a) through (d) change if the person were female? Why or why not? (f) A friend of yours uses your sample of data and estimates the following regression: In (w) = 0.6+0.12 Educ+0.33 Male -0.02(Malex Educ) +0.03Exper-0.0005 Exper², where Male is a male dummy variable and the other variables are defined as in your regression. Use your friend's output to estimate all the coefficients in your regression, i.e., find b, 6₁, 6₂, 63, 64, and b5. 0 1. 21 (g) Write down the regression that you will estimate if you suspected that the effect of potential experience on earnings was different for men and women. You estimate the following regression: In(w) = b + b Educ+b Female +b(Female × Educ) + b_Exper+ b Exper², where w is average hourly earnings, Female is a female dummy variable, Educ is years of education, Exper is potential experience (number of years since completion of schooling). You estimate the regression on a large sample of data and find that all coefficients are statistically significant at the 1% level. (a) Consider a male with 18 years of education and 3 years of experience. Express the predicted change in In(w) associated with an additional year of experience as a function of the estimated regression coefficients. (b) Let 4ln(w) denote the predicted change you estimated in (a). A 95% confidence interval for Aln(w) would equal Aln(w) ± 1.96× se (Aln(w)). Rearrange the regression so that se (Aln(w)) is the standard error of an estimated coefficient in the transformed regression. (c) Consider a male with 18 years of education and 4 years of experience. Express the predicted change in In(w) associated with an additional year of experience as a function of the estimated regression coefficients. (d) Is the difference between your answers to (c) and (a) statistically significant at the 5% level? Why or why not? (e) Would your answers to (a) through (d) change if the person were female? Why or why not? (f) A friend of yours uses your sample of data and estimates the following regression: In (w) = 0.6+0.12 Educ+0.33 Male -0.02(Malex Educ) +0.03Exper-0.0005 Exper², where Male is a male dummy variable and the other variables are defined as in your regression. Use your friend's output to estimate all the coefficients in your regression, i.e., find b, 6₁, 6₂, 63, 64, and b5. 0 1. 21 (g) Write down the regression that you will estimate if you suspected that the effect of potential experience on earnings was different for men and women.

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In w bo b edu b male by malex Edu by exper bs Expe... View the full answer