------------------------------------------------------------------------------------------------ name: log: C:\Users\Professor\Desktop\aula20_09.log log type: text opened on: 20 Sep 2022, 14:41:33 . reg wage educ exper female Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 77.92 Model | 2214.74206 3 738.247353 Prob > F = 0.0000 Residual | 4945.67223 522 9.47446788 R-squared = 0.3093 -------------+---------------------------------- Adj R-squared = 0.3053 Total | 7160.41429 525 13.6388844 Root MSE = 3.0781 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .6025802 .0511174 11.79 0.000 .5021591 .7030012 exper | .0642417 .0104003 6.18 0.000 .0438101 .0846734 female | -2.155517 .2703055 -7.97 0.000 -2.686537 -1.624497 _cons | -1.734481 .7536203 -2.30 0.022 -3.214982 -.2539797 ------------------------------------------------------------------------------ . predict u, res variable u already defined r(110); . gen u2=u^2 variable u2 already defined r(110); . reg u2 educ exper female Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 13.13 Model | 22587.9402 3 7529.31342 Prob > F = 0.0000 Residual | 299359.526 522 573.485683 R-squared = 0.0702 -------------+---------------------------------- Adj R-squared = 0.0648 Total | 321947.467 525 613.23327 Root MSE = 23.948 ------------------------------------------------------------------------------ u2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | 1.881484 .3976972 4.73 0.000 1.1002 2.662767 exper | .2694904 .0809153 3.33 0.001 .1105307 .4284501 female | -6.365282 2.102997 -3.03 0.003 -10.49666 -2.233904 _cons | -15.77059 5.863223 -2.69 0.007 -27.289 -4.252172 ------------------------------------------------------------------------------ . reg wage educ exper female Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 77.92 Model | 2214.74206 3 738.247353 Prob > F = 0.0000 Residual | 4945.67223 522 9.47446788 R-squared = 0.3093 -------------+---------------------------------- Adj R-squared = 0.3053 Total | 7160.41429 525 13.6388844 Root MSE = 3.0781 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .6025802 .0511174 11.79 0.000 .5021591 .7030012 exper | .0642417 .0104003 6.18 0.000 .0438101 .0846734 female | -2.155517 .2703055 -7.97 0.000 -2.686537 -1.624497 _cons | -1.734481 .7536203 -2.30 0.022 -3.214982 -.2539797 ------------------------------------------------------------------------------ . estat hettest, rhs iid Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: educ exper female chi2(3) = 36.90 Prob > chi2 = 0.0000 . estat imtest, preserve white White's test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity chi2(8) = 63.41 Prob > chi2 = 0.0000 Cameron & Trivedi's decomposition of IM-test --------------------------------------------------- Source | chi2 df p ---------------------+----------------------------- Heteroskedasticity | 63.41 8 0.0000 Skewness | 31.28 3 0.0000 Kurtosis | 9.61 1 0.0019 ---------------------+----------------------------- Total | 104.31 12 0.0000 --------------------------------------------------- . reg wage educ exper female Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 77.92 Model | 2214.74206 3 738.247353 Prob > F = 0.0000 Residual | 4945.67223 522 9.47446788 R-squared = 0.3093 -------------+---------------------------------- Adj R-squared = 0.3053 Total | 7160.41429 525 13.6388844 Root MSE = 3.0781 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .6025802 .0511174 11.79 0.000 .5021591 .7030012 exper | .0642417 .0104003 6.18 0.000 .0438101 .0846734 female | -2.155517 .2703055 -7.97 0.000 -2.686537 -1.624497 _cons | -1.734481 .7536203 -2.30 0.022 -3.214982 -.2539797 ------------------------------------------------------------------------------ . predict wagehat variable wagehat already defined r(110); . gen wagehat2=wagehat^2 variable wagehat2 already defined r(110); . reg u2 wagehat wagehat2 Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(2, 523) = 27.74 Model | 30878.5201 2 15439.26 Prob > F = 0.0000 Residual | 291068.946 523 556.537183 R-squared = 0.0959 -------------+---------------------------------- Adj R-squared = 0.0925 Total | 321947.467 525 613.23327 Root MSE = 23.591 ------------------------------------------------------------------------------ u2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wagehat | -4.664488 2.035402 -2.29 0.022 -8.663055 -.6659208 wagehat2 | .671918 .1693937 3.97 0.000 .3391423 1.004694 _cons | 10.71701 5.923432 1.81 0.071 -.9196321 22.35365 ------------------------------------------------------------------------------ . test wagehat wagehat2 ( 1) wagehat = 0 ( 2) wagehat2 = 0 F( 2, 523) = 27.74 Prob > F = 0.0000 . gen yhat=educ*.6025802+ exper*.0642417+female*-2.155517 . replace yhat=educ*.6025802+ exper*.0642417+female*-2.155517-1.734481 (526 real changes made) . twoway (scatter wage educ) . twoway (scatter wage educ) (scatter wagehat educ) . twoway (scatter wage educ) (scatter wagehat educ) . reg wage educ exper female, robust Linear regression Number of obs = 526 F(3, 522) = 51.41 Prob > F = 0.0000 R-squared = 0.3093 Root MSE = 3.0781 ------------------------------------------------------------------------------ | Robust wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .6025802 .0641895 9.39 0.000 .4764786 .7286817 exper | .0642417 .010044 6.40 0.000 .0445102 .0839733 female | -2.155517 .2591294 -8.32 0.000 -2.664582 -1.646453 _cons | -1.734481 .8577554 -2.02 0.044 -3.419558 -.0494042 ------------------------------------------------------------------------------ . twoway (scatter wage educ) (scatter wagehat educ) . twoway (scatter wage educ) (scatter wagehat educ) . twoway (scatter wage educ) (scatter wagehat educ) . reg wage educ exper female Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 77.92 Model | 2214.74206 3 738.247353 Prob > F = 0.0000 Residual | 4945.67223 522 9.47446788 R-squared = 0.3093 -------------+---------------------------------- Adj R-squared = 0.3053 Total | 7160.41429 525 13.6388844 Root MSE = 3.0781 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .6025802 .0511174 11.79 0.000 .5021591 .7030012 exper | .0642417 .0104003 6.18 0.000 .0438101 .0846734 female | -2.155517 .2703055 -7.97 0.000 -2.686537 -1.624497 _cons | -1.734481 .7536203 -2.30 0.022 -3.214982 -.2539797 ------------------------------------------------------------------------------ . predict residuo, res . gen residuo2=residuo^2 . gen lresiduo2=log(residuo2) . reg lresiduo2 educ exper female Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 22.59 Model | 415.266537 3 138.422179 Prob > F = 0.0000 Residual | 3198.438 522 6.12727587 R-squared = 0.1149 -------------+---------------------------------- Adj R-squared = 0.1098 Total | 3613.70454 525 6.88324674 Root MSE = 2.4753 ------------------------------------------------------------------------------ lresiduo2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .2126023 .0411079 5.17 0.000 .1318451 .2933594 exper | .0456081 .0083638 5.45 0.000 .0291772 .0620389 female | -.9165781 .2173758 -4.22 0.000 -1.343617 -.4895393 _cons | -2.553791 .6060506 -4.21 0.000 -3.744389 -1.363194 ------------------------------------------------------------------------------ . predict lresiduo2hat (option xb assumed; fitted values) . gen residuo2hat=exp(lresiduo2hat) . reg wage educ exper female [aweight=1/residuo2hat] (sum of wgt is 491.9088728647671) Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(3, 522) = 52.84 Model | 781.57755 3 260.52585 Prob > F = 0.0000 Residual | 2573.93923 522 4.93091807 R-squared = 0.2329 -------------+---------------------------------- Adj R-squared = 0.2285 Total | 3355.51678 525 6.39146054 Root MSE = 2.2206 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .3029611 .0326091 9.29 0.000 .2388999 .3670223 exper | .0571835 .0090676 6.31 0.000 .03937 .074997 female | -1.47104 .2198003 -6.69 0.000 -1.902841 -1.039238 _cons | 1.520901 .4715785 3.23 0.001 .5944765 2.447326 ------------------------------------------------------------------------------ . predict wagehat_fgls (option xb assumed; fitted values) . twoway (scatter wage educ) (scatter wagehat educ) (scatter wagehat_fgls educ) . gen female_educ=female*educ . gen female_exper=female*exper . reg wage educ exper female female_educ female_exper Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(5, 520) = 53.18 Model | 2422.6316 5 484.526319 Prob > F = 0.0000 Residual | 4737.7827 520 9.11112057 R-squared = 0.3383 -------------+---------------------------------- Adj R-squared = 0.3320 Total | 7160.41429 525 13.6388844 Root MSE = 3.0185 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .7254958 .0653954 11.09 0.000 .5970242 .8539674 exper | .1128012 .0145464 7.75 0.000 .0842243 .141378 female | 2.585399 1.449019 1.78 0.075 -.261252 5.432049 female_educ | -.2494 .1026407 -2.43 0.015 -.4510414 -.0477586 female_exper | -.0947253 .0204177 -4.64 0.000 -.1348366 -.054614 _cons | -4.158992 .9770286 -4.26 0.000 -6.0784 -2.239584 ------------------------------------------------------------------------------ . display .7254958 -.2494 .4760958 . display .1128012 -.0947253 .0180759 . reg wage educ exper female_educ female_exper Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(4, 521) = 65.40 Model | 2393.62621 4 598.406552 Prob > F = 0.0000 Residual | 4766.78808 521 9.14930534 R-squared = 0.3343 -------------+---------------------------------- Adj R-squared = 0.3292 Total | 7160.41429 525 13.6388844 Root MSE = 3.0248 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .6506094 .0502546 12.95 0.000 .5518828 .749336 exper | .1027316 .013435 7.65 0.000 .0763381 .1291251 female_educ | -.0743934 .0303043 -2.45 0.014 -.1339269 -.0148599 female_exper | -.0766069 .0177505 -4.32 0.000 -.1114782 -.0417356 _cons | -2.98357 .7230321 -4.13 0.000 -4.403986 -1.563153 ------------------------------------------------------------------------------ . reg wage educ exper female female_educ female_exper Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(5, 520) = 53.18 Model | 2422.6316 5 484.526319 Prob > F = 0.0000 Residual | 4737.7827 520 9.11112057 R-squared = 0.3383 -------------+---------------------------------- Adj R-squared = 0.3320 Total | 7160.41429 525 13.6388844 Root MSE = 3.0185 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .7254958 .0653954 11.09 0.000 .5970242 .8539674 exper | .1128012 .0145464 7.75 0.000 .0842243 .141378 female | 2.585399 1.449019 1.78 0.075 -.261252 5.432049 female_educ | -.2494 .1026407 -2.43 0.015 -.4510414 -.0477586 female_exper | -.0947253 .0204177 -4.64 0.000 -.1348366 -.054614 _cons | -4.158992 .9770286 -4.26 0.000 -6.0784 -2.239584 ------------------------------------------------------------------------------ . test female female_educ female_exper ( 1) female = 0 ( 2) female_educ = 0 ( 3) female_exper = 0 F( 3, 520) = 29.65 Prob > F = 0.0000 . reg wage educ Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(1, 524) = 103.36 Model | 1179.73204 1 1179.73204 Prob > F = 0.0000 Residual | 5980.68225 524 11.4135158 R-squared = 0.1648 -------------+---------------------------------- Adj R-squared = 0.1632 Total | 7160.41429 525 13.6388844 Root MSE = 3.3784 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .5413593 .053248 10.17 0.000 .4367534 .6459651 _cons | -.9048516 .6849678 -1.32 0.187 -2.250472 .4407687 ------------------------------------------------------------------------------ . gen leduc=log(educ) (2 missing values generated) . reg lwage educ Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(1, 524) = 119.58 Model | 27.5606288 1 27.5606288 Prob > F = 0.0000 Residual | 120.769123 524 .230475425 R-squared = 0.1858 -------------+---------------------------------- Adj R-squared = 0.1843 Total | 148.329751 525 .28253286 Root MSE = .48008 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0827444 .0075667 10.94 0.000 .0678796 .0976091 _cons | .5837727 .0973358 6.00 0.000 .3925563 .7749891 ------------------------------------------------------------------------------ . reg wage leduc Source | SS df MS Number of obs = 524 -------------+---------------------------------- F(1, 522) = 76.85 Model | 917.33098 1 917.33098 Prob > F = 0.0000 Residual | 6231.02449 522 11.9368285 R-squared = 0.1283 -------------+---------------------------------- Adj R-squared = 0.1267 Total | 7148.35547 523 13.6679837 Root MSE = 3.455 ------------------------------------------------------------------------------ wage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- leduc | 5.329674 .60797 8.77 0.000 4.135306 6.524043 _cons | -7.460211 1.532073 -4.87 0.000 -10.47 -4.450426 ------------------------------------------------------------------------------ . reg lwage leduc Source | SS df MS Number of obs = 524 -------------+---------------------------------- F(1, 522) = 91.12 Model | 21.9912877 1 21.9912877 Prob > F = 0.0000 Residual | 125.983349 522 .241347412 R-squared = 0.1486 -------------+---------------------------------- Adj R-squared = 0.1470 Total | 147.974637 523 .282934296 Root MSE = .49127 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- leduc | .8252071 .0864488 9.55 0.000 .6553768 .9950374 _cons | -.4446768 .2178493 -2.04 0.042 -.8726459 -.0167077 ------------------------------------------------------------------------------ . lwage educ command lwage is unrecognized r(199); . reg lwage educ Source | SS df MS Number of obs = 526 -------------+---------------------------------- F(1, 524) = 119.58 Model | 27.5606288 1 27.5606288 Prob > F = 0.0000 Residual | 120.769123 524 .230475425 R-squared = 0.1858 -------------+---------------------------------- Adj R-squared = 0.1843 Total | 148.329751 525 .28253286 Root MSE = .48008 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0827444 .0075667 10.94 0.000 .0678796 .0976091 _cons | .5837727 .0973358 6.00 0.000 .3925563 .7749891 ------------------------------------------------------------------------------ . log close name: log: C:\Users\Professor\Desktop\aula20_09.log log type: text closed on: 20 Sep 2022, 17:11:00 ------------------------------------------------------------------------------------------------