İnsan TUNALI 8 November 2018 Econ 511: Econometrics I. ASSIGNMENT 7 STATA Supplement
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1 İnsan TUNALI 8 November 2018 Econ 511: Econometrics I ASSIGNMENT 7 STATA Supplement. use "F:\COURSES\GRADS\ECON511\SHARE\wages1.dta", clear. generate =ln(wage). scatter sch Q. Do you see a relationship between log-wages and years of ing? A. NO.
2 . tab male, sum(wage) Summary of wage male Mean Std. Dev. Freq Total * On average males earn more than females; raw wage gap is = 1.16 $/hr.. tab male,sum(sch) Summary of male Mean Std. Dev. Freq Total * On average females have a little bit more ing than females. * Interact with male dummy:. generate sch_m=*male * RETURNS TO SCHOOLING:. regress F( 1, 3292) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] _cons * An additonal year of ing increases wages by 11%.
3 . predict yhat (option xb assumed; fitted values). scatter yhat Fitted values * Earlier you looked at:. scatter sch Q. Are there any surprises here? A. NO: yhat is an exact linear combination of sch. Also see Fall 2014 Midterm Exam Part IV (2).
4 . scatter yhat, connect(l) ytitle("yhat") yhat scatter yhat, connect(. l) ytitle("yhat") yhat Fitted values
5 * ADDITIVE SHIFT (AS) MODEL:. regress male F( 2, 3291) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] male _cons test = coef in SR ( 1) = F( 1, 3291) = 2.28 Prob > F = * The point estimate of returns to ing is a bit higher, but statistically speaking not any different form that found in the simple regression of on sch.. predict yhatas (option xb assumed; fitted values). scatter yhatas, connect(l) ytitle("yhatas") yhatas
6 * SWITCHING REGRESSION (SR) MODEL: * Full interaction version:. regress male sch_m F( 3, 3290) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] male sch_m _cons predict yhatsr (option xb assumed; fitted values) * Separate regressions version:. regress if male==0 Source SS df MS Number of obs = F( 1, 1567) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] _cons regress if male==1 Source SS df MS Number of obs = F( 1, 1723) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] _cons * The point estimates of the returns to ing is higher by about 1% for females. But the slope difference is not statisticaly significant at conventional levels.
7 . scatter yhatsr, connect(l) ytitle("yhatsr") yhatsr scatter yhatas, connect(l) ytitle("yhatas") yhatas
8 * EXPERIENCE PROFILE:. scatter exper 20 exper Q. Do you see a relationship between log-wages and years of experience? A. NO. * LINEAR REGRESSION:. regress exper F( 1, 3292) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] exper _cons predict yhatlr (option xb assumed; fitted values)
9 . scatter yhatlr exper,connect(. l) ytitle("yhatlr") yhatlr 20 exper Fitted values * QUADRATIC REGRESSION:. generate expsq=exper^2/100 * SHORT: ( omitted). regress exper expsq F( 2, 3291) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] exper expsq _cons * There is very strong evidence that the experience profile is quadratic. Linear term > 0, quadratic term < 0 Concave profile. FOC:.165 2*0.85exp/100 = 0; exp* = 100(.165)/(2*0.85) = 9.7 years. SOC: < 0 we have a max.
10 . predict yhatqr (option xb assumed; fitted values). predict resex,residuals. scatter yhatqr exper,connect(. l) ytitle("yhatqr") sort yhatqr 20 exper Fitted values * Indeed peak is reached around exp = 9.7. * AUXILIARY REGRESSION OF on exp expsq:. regress exper expsq F( 2, 3291) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] exper expsq _cons predict ressch,residuals
11 * RESIDUAL REGRESSION RULE: * LONG:. regress exper expsq F( 3, 3290) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] exper expsq _cons regress ressch F( 1, 3292) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] ressch _cons regress resex ressch F( 1, 3292) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = resex Coef. Std. Err. t P> t [95% Conf. Interval] ressch _cons 2.44e sum resex ressch Variable Obs Mean Std. Dev. Min Max resex e ressch e * SLOPES are the same! (Why?)
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