# GETTING STARTED: STATA & R BASIC COMMANDS ECONOMETRICS II. Stata Output Regression of wages on education

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1 GETTING STARTED: STATA & R BASIC COMMANDS ECONOMETRICS II Stata Output Regression of wages on education. sum wage educ Variable Obs Mean Std. Dev. Min Max wage educ reg wage educ Source SS df MS Number of obs F( 1, 524) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE wage Coef. Std. Err. t P> t [95% Conf. Interval] educ _cons Do File Regression of wages on education * clear log using lecture1.log, replace use Wage1.dta sum wage educ reg wage educ log close * R Output Regression of wages on education > data <- read.csv( YOUR PATH, headert) > data <- data.frame(data) > summary(data)[,1:2] wage educ "Min. : " "Min. : 0.00 " "1st Qu.: " "1st Qu.:12.00 " "Median : " "Median :12.00 " "Mean : " "Mean :12.56 " "3rd Qu.: " "3rd Qu.:14.00 " "Max. : " "Max. :18.00 " > fit <- lm(data\$wage ~ data\$educ)

2 > summary(fit) Call: lm(formula data\$wage ~ data\$educ) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) data\$educ <2e-16 *** --- Signif. codes: 0 `***' `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: on 524 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 1 and 524 DF, p-value: < 2.2e-16 R text File Regression of wages on education # data <- read.csv("your PATH", headert) data <- data.frame(data) # # Figure 1, Lecture 1: Scartterplot # postscript("wage_fig1.ps", horizontal FALSE, height6.5,width6.5) par(mfrowc(1,1)) plot(data\$educ,data\$wage,,type"n",xlab "years of education", ylab"average hourly earnings") points(data\$educ,data\$wage,pch19,cex0.5) dev.off() # summary(data)[,1:2] fit <- lm(data\$wage ~ data\$educ) summary(fit) #

3 Stata Output Regression of wages on education, experience and tenure. use Wage1.dta. reg lwage educ exper expersq tenure Source SS df MS Number of obs F( 4, 521) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ exper expersq tenure _cons display "Number of Observations " _result(1) Number of Observations 526. display "R2 " _result(7) R vce educ exper expersq tenure _cons educ exper -1.7e expersq 1.2e e e-08 tenure -2.2e e e e-06 _cons e predict yhat (option xb assumed; fitted values). predict uhat, resid. sum uhat Variable Obs Mean Std. Dev. Min Max uhat e R Output Regression of wages on education, experience and tenure > data <- read.csv( YOUR PATH", headert) > data <- data.frame(data) > fit <- lm(data\$lwage ~ data\$educ + data\$exper + data\$expersq + data\$tenure) > summary(fit)

4 Call: lm(formula data\$lwage ~ data\$educ + data\$exper + data\$expersq + data\$tenure) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) data\$educ < 2e-16 *** data\$exper e-10 *** data\$expersq e-09 *** data\$tenure e-11 *** --- Signif. codes: 0 `***' `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: on 521 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 4 and 521 DF, p-value: < 2.2e-16 > length(data\$educ) [1] 526 > vcov(fit) (Intercept) data\$educ data\$exper data\$expersq (Intercept) data\$educ e e e e e e e e-07 data\$exper e e e e-07 data\$expersq e e e e-08 data\$tenure e e e e-08 data\$tenure (Intercept) data\$educ e e-06 data\$exper e-06 data\$expersq e-08 data\$tenure e-06 > fit\$fitted -> yhat #or > fitted(fit) -> yhat > fit\$resid -> uhat

5 . use Wage1.dta. reg lwage educ exper tenure Source SS df MS Number of obs F( 3, 522) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ exper tenure _cons test educ ( 1) educ 0 F( 1, 522) Prob > F 0. test educ 0.1 ( 1) educ.1 F( 1, 522) 1.18 Prob > F test exper tenure ( 1) exper 0 ( 2) tenure 0 F( 2, 522) Prob > F 0. test exper tenure ( 1) exper - tenure 0 F( 1, 522) Prob > F 0 R Output > fit <- lm(data\$lwage ~ data\$educ + data\$exper + data\$tenure) > summary(fit) Call: lm(formula data\$lwage ~ data\$educ + data\$exper + data\$tenure) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) data\$educ ** < 2e-16 *** data\$exper * data\$tenure e-12 ***

6 --- Signif. codes: 0 `***' `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: on 522 degrees of freedom Multiple R-Squared: 0.316, Adjusted R-squared: F-statistic: on 3 and 522 DF, p-value: < 2.2e-16 > # > fit.u <- lm(data\$lwage ~ data\$educ + data\$exper + data\$tenure) > fit.r <- lm(data\$lwage ~ data\$exper + data\$tenure) > F.test(fit.u,fit.r) \$F [1] \$Prob [1] 0 > # > fit.u <- lm(data\$lwage ~ data\$educ + data\$exper + data\$tenure) > fit.r <- lm((data\$lwage - 0.1*data\$educ) ~ data\$exper + data\$tenure) > F.test(fit.u,fit.r) \$F [1] \$Prob [1] > # > fit.u <- lm(data\$lwage ~ data\$educ + data\$exper + data\$tenure) > fit.r <- lm(data\$lwage ~ data\$educ) > F.test(fit.u,fit.r) \$F [1] \$Prob [1] 0 > # > fit.u <- lm(data\$lwage ~ data\$educ + data\$exper + data\$tenure) > x <- (data\$exper + data\$tenure) > fit.r <- lm(data\$lwage ~ data\$educ + x) > F.test(fit.u,fit.r) \$F [1] \$Prob [1] e-05

7 Panel Data Methods - Stata Output. clear. use Jtrain.dta. tis year. iis fcode. sort fcode. quietly by fcode: gen lscrap1 lscrap[_n-1]. gen lscrapd lscrap - lscrap1 (363 missing values generated). quietly by fcode: gen grant1 grant[_n-1]. gen grantd grant - grant1 (157 missing values generated). quietly by fcode: gen grant1_1 grant_1[_n-1]. gen grant_1d lscrap - grant1_1 (363 missing values generated) First Difference Estimator:. reg lscrapd grantd grant_1d Source SS df MS Number of obs F( 2, 105) 3.54 Model Prob > F Residual R-squared Adj R-squared Total Root MSE lscrapd Coef. Std. Err. t P> t [95% Conf. Interval] grantd grant_1d _cons xtreg lscrap grant grant_1, fe Fixed-effects (within) regression Number of obs 162 Group variable (i): fcode Number of groups 54 R-sq: within Obs per group: min 3 between overall avg max F(2,106) corr(u_i, Xb) Prob > F 0 lscrap Coef. Std. Err. t P> t [95% Conf. Interval] grant grant_ _cons

8 sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i0: F(53, 106) Prob > F 0. xtreg lscrap grant grant_1 union, fe Fixed-effects (within) regression Number of obs 162 Group variable (i): fcode Number of groups 54 R-sq: within Obs per group: min 3 between overall avg max F(2,106) corr(u_i, Xb) Prob > F 0 lscrap Coef. Std. Err. t P> t [95% Conf. Interval] grant grant_ union (dropped) _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i0: F(53, 106) Prob > F 0. xtreg lscrap grant grant_1, re Random-effects GLS regression Number of obs 162 Group variable (i): fcode Number of groups 54 R-sq: within between Obs per group: min avg overall max 3 Random effects u_i ~ Gaussian Wald chi2(2) corr(u_i, X) 0 (assumed) Prob > chi2 0 lscrap Coef. Std. Err. z P> z [95% Conf. Interval] grant grant_ _cons sigma_u sigma_e rho (fraction of variance due to u_i). xtreg lscrap grant grant_1 d88 d89, fe Fixed-effects (within) regression Number of obs 162 Group variable (i): fcode Number of groups 54

9 R-sq: within between Obs per group: min avg overall max 3 F(4,104) 6.54 corr(u_i, Xb) Prob > F 1 lscrap Coef. Std. Err. t P> t [95% Conf. Interval] grant grant_ d d _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i0: F(53, 104) Prob > F 0. hausman, save. xtreg lscrap grant grant_1 d88 d89, re Random-effects GLS regression Number of obs 162 Group variable (i): fcode Number of groups 54 R-sq: within Obs per group: min 3 between overall avg max Random effects u_i ~ Gaussian Wald chi2(4) corr(u_i, X) 0 (assumed) Prob > chi2 0 lscrap Coef. Std. Err. z P> z [95% Conf. Interval] grant grant_ d d _cons sigma_u sigma_e rho (fraction of variance due to u_i). hausman ---- Coefficients ---- (b) (B) (b-b) sqrt(diag(v_b-v_b)) Consistent Efficient Difference S.E. grant grant_1 d d b consistent under Ho and Ha; obtained from xtreg B inconsistent under Ha, efficient under Ho; obtained from xtreg

10 Test: Ho: difference in coefficients not systematic chi2(4) (b-b)'[(v_b-v_b)^(-1)](b-b) 2.14 Prob>chi

11 IV & 2SLS Methods - Stata Output Do file for IV/2SLS lecture.. clear. use Mroz.dta IV. reg lwage educ Source SS df MS Number of obs F( 1, 426) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ _cons ivreg lwage (educfatheduc) Instrumental variables (2SLS) regression Source SS df MS Number of obs F( 1, 426) 2.84 Model Prob > F Residual R-squared Adj R-squared Total Root MSE educ _cons Instrumented: educ Instruments: fatheduc 2SLS. regress lwage educ exper expersq Source SS df MS Number of obs F( 3, 424) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ

12 exper expersq _cons regress lwage educ exper expersq (exper expersq motheduc fatheduc) Instrumental variables (2SLS) regression Source SS df MS Number of obs F( 3, 424) 8.14 Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ exper expersq _cons Tests: (1) poor instruments:. reg educ exper expersq motheduc fatheduc Source SS df MS Number of obs F( 4, 748) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ Coef. Std. Err. t P> t [95% Conf. Interval] exper expersq motheduc fatheduc _cons test motheduc fatheduc ( 1) motheduc 0 ( 2) fatheduc 0 F( 2, 748) Prob > F 0 (2) over identifying restrictions:. regress lwage educ exper expersq (exper expersq motheduc fatheduc) Instrumental variables (2SLS) regression Source SS df MS Number of obs 428

13 F( 3, 424) 8.14 Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ exper expersq _cons predict u2sls, resid (325 missing values generated). regress u2sls exper expersq motheduc fatheduc Source SS df MS Number of obs F( 4, 423) 0.09 Model Prob > F Residual R-squared Adj R-squared Total Root MSE u2sls Coef. Std. Err. t P> t [95% Conf. Interval] exper expersq 7.34e-07 motheduc fatheduc _cons test motheduc fatheduc ( 1) motheduc 0 ( 2) fatheduc 0 F( 2, 423) 0.19 Prob > F

14 . (3) Hausman test:. regress lwage educ exper expersq (exper expersq motheduc fatheduc) Instrumental variables (2SLS) regression Source SS df MS Number of obs F( 3, 424) 8.14 Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ exper expersq _cons hausman, save. regress lwage educ exper expersq Source SS df MS Number of obs F( 3, 424) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE educ exper expersq _cons hausman ---- Coefficients ---- (b) Consistent (B) Efficient (b-b) Difference sqrt(diag(v_b-v_b)) S.E. educ exper expersq b consistent under Ho and Ha; obtained from regress B inconsistent under Ha, efficient under Ho; obtained from regress Test: Ho: difference in coefficients not systematic chi2(3) (b-b)'[(v_b-v_b)^(-1)](b-b) 2.70 Prob>chi

15 Probit, Logit & Tobit Methods Stata Output. clear. use Mroz.dta Linear Probability Model. reg inlf nwifeinc educ exper expersq Source SS df MS Number of obs F( 4, 748) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE inlf Coef. Std. Err. t P> t [95% Conf. Interval] nwifeinc educ exper expersq _cons mfx Marginal effects after regress y Fitted values (predict) variable dy/dx Std. Err. z P> z [ 95% C.I. ] X nwifeinc educ exper expersq Probit.. probit inlf nwifeinc educ exper expersq Iteration 0: log likelihood Iteration 1: Iteration 2: log likelihood log likelihood Iteration 3: log likelihood Probit estimates Number of obs 753 LR chi2(4) Log likelihood Prob > chi2 Pseudo R inlf Coef. Std. Err. z P> z [95% Conf. Interval]

16 nwifeinc educ exper expersq _cons mfx Marginal effects after probit y Pr(inlf) (predict) variable dy/dx Std. Err. z P> z [ 95% C.I. ] X nwifeinc educ exper expersq Logit.. logit inlf nwifeinc educ exper expersq Iteration 0: log likelihood Iteration 1: log likelihood Iteration 2: Iteration 3: log likelihood log likelihood Logit estimates Number of obs LR chi2(4) Prob > chi2 0 Log likelihood Pseudo R inlf Coef. Std. Err. z P> z [95% Conf. Interval] nwifeinc educ exper expersq _cons mfx Marginal effects after logit y Pr(inlf) (predict) variable dy/dx Std. Err. z P> z [ 95% C.I. ] X nwifeinc educ exper expersq tobit faminc educ exper expersq, ul(100000) Tobit estimates Number of obs 753

17 LR chi2(3) Log likelihood Prob > chi2 Pseudo R faminc Coef. Std. Err. t P> t [95% Conf. Interval] educ exper expersq _cons _se (Ancillary parameter) Obs. summary: 753 uncensored observations Heckman Correction - Stata Output. logit inlf nwifeinc educ exper expersq age kidslt6 kidsge6 Iteration 0: log likelihood Iteration 1: log likelihood Iteration 2: Iteration 3: log likelihood log likelihood Iteration 4: log likelihood Logit estimates Number of obs 753 LR chi2(7) Log likelihood Prob > chi2 Pseudo R inlf Coef. Std. Err. z P> z [95% Conf. Interval] nwifeinc educ exper expersq age kidslt kidsge6 _cons predict xb, xb. gen smallphinormd(xb). gen largephinormprob(xb). gen lambdasmallphi/largephi. reg lwage educ exper expersq lambda if inlf1 Source SS df MS Number of obs F( 4, 423) Model Prob > F 0 Residual R-squared Adj R-squared Total Root MSE.66708

18 educ exper expersq lambda _cons heckman lwage educ exper expersq, select(inlf nwifeinc educ exper expersq age kidslt6 kidsge > 6) twostep Heckman selection model -- two-step estimates (regression model with sample selection) Number of obs Censored obs Uncensored obs 428 Wald chi2(6) Prob > chi2 0 Coef. Std. Err. z P> z [95% Conf. Interval] lwage educ exper expersq e-06 _cons inlf nwifeinc educ exper expersq age kidslt kidsge _cons mills lambda rho sigma lambda

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