---------------------------------------------------------------------------------------------------------------- log: C:\EC471\2sls_mroz.log log type: text opened on: 6 Apr 2004, 15:18:30 (1) OLS: biased . regress lwage educ exper expersq Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 3, 424) = 26.29 Model | 35.0223023 3 11.6741008 Prob > F = 0.0000 Residual | 188.305149 424 .444115917 R-squared = 0.1568 -------------+------------------------------ Adj R-squared = 0.1509 Total | 223.327451 427 .523015108 Root MSE = .66642 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .1074896 .0141465 7.60 0.000 .0796837 .1352956 exper | .0415665 .0131752 3.15 0.002 .0156697 .0674633 expersq | -.0008112 .0003932 -2.06 0.040 -.0015841 -.0000382 _cons | -.5220407 .1986321 -2.63 0.009 -.9124668 -.1316145 ------------------------------------------------------------------------------ (2) Hausman test . regress educ exper expersq motheduc fatheduc huseduc Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 5, 422) = 63.30 Model | 955.830608 5 191.166122 Prob > F = 0.0000 Residual | 1274.36565 422 3.01982382 R-squared = 0.4286 -------------+------------------------------ Adj R-squared = 0.4218 Total | 2230.19626 427 5.22294206 Root MSE = 1.7378 ------------------------------------------------------------------------------ educ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- exper | .0374977 .0343102 1.09 0.275 -.0299424 .1049379 expersq | -.0006002 .0010261 -0.58 0.559 -.0026171 .0014167 motheduc | .1141532 .0307835 3.71 0.000 .0536452 .1746613 fatheduc | .1060801 .0295153 3.59 0.000 .0480648 .1640955 huseduc | .3752548 .0296347 12.66 0.000 .3170049 .4335048 _cons | 5.538311 .4597824 12.05 0.000 4.634562 6.44206 ------------------------------------------------------------------------------ . predict edu_res, res . regress lwage educ exper expersq edu_res Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 4, 423) = 20.48 Model | 36.230504 4 9.05762599 Prob > F = 0.0000 Residual | 187.096947 423 .442309568 R-squared = 0.1622 -------------+------------------------------ Adj R-squared = 0.1543 Total | 223.327451 427 .523015108 Root MSE = .66506 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0803918 .0216362 3.72 0.000 .0378639 .1229197 exper | .0430973 .013181 3.27 0.001 .017189 .0690057 expersq | -.0008628 .0003937 -2.19 0.029 -.0016366 -.000089 edu_res | .047189 .0285519 1.65 0.099 -.0089322 .1033102 _cons | -.1868574 .2835905 -0.66 0.510 -.7442794 .3705647 ------------------------------------------------------------------------------ . test edu_res ( 1) edu_res = 0 F( 1, 423) = 2.73 Prob > F = 0.0991 (3) 2SLS . ivreg lwage (educ = motheduc fatheduc huseduc) exper expersq Instrumental variables (2SLS) regression Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 3, 424) = 11.52 Model | 33.3927427 3 11.1309142 Prob > F = 0.0000 Residual | 189.934709 424 .447959218 R-squared = 0.1495 -------------+------------------------------ Adj R-squared = 0.1435 Total | 223.327451 427 .523015108 Root MSE = .6693 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0803918 .021774 3.69 0.000 .0375934 .1231901 exper | .0430973 .0132649 3.25 0.001 .0170242 .0691704 expersq | -.0008628 .0003962 -2.18 0.030 -.0016415 -.0000841 _cons | -.1868574 .2853959 -0.65 0.513 -.7478243 .3741096 ------------------------------------------------------------------------------ Instrumented: educ Instruments: exper expersq motheduc fatheduc huseduc ------------------------------------------------------------------------------ (4) My own 2 stages 1st stage . regress educ exper expersq motheduc fatheduc huseduc Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 5, 422) = 63.30 Model | 955.830608 5 191.166122 Prob > F = 0.0000 Residual | 1274.36565 422 3.01982382 R-squared = 0.4286 -------------+------------------------------ Adj R-squared = 0.4218 Total | 2230.19626 427 5.22294206 Root MSE = 1.7378 ------------------------------------------------------------------------------ educ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- exper | .0374977 .0343102 1.09 0.275 -.0299424 .1049379 expersq | -.0006002 .0010261 -0.58 0.559 -.0026171 .0014167 motheduc | .1141532 .0307835 3.71 0.000 .0536452 .1746613 fatheduc | .1060801 .0295153 3.59 0.000 .0480648 .1640955 huseduc | .3752548 .0296347 12.66 0.000 .3170049 .4335048 _cons | 5.538311 .4597824 12.05 0.000 4.634562 6.44206 ------------------------------------------------------------------------------ . predict edu_pre (option xb assumed; fitted values) 2nd stage . regress lwage edu_pre exper expersq edu_res Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 4, 423) = 20.48 Model | 36.2305033 4 9.05762583 Prob > F = 0.0000 Residual | 187.096948 423 .44230957 R-squared = 0.1622 -------------+------------------------------ Adj R-squared = 0.1543 Total | 223.327451 427 .523015108 Root MSE = .66506 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- edu_pre | .0803918 .0216362 3.72 0.000 .0378639 .1229197 exper | .0430973 .013181 3.27 0.001 .017189 .0690057 expersq | -.0008628 .0003937 -2.19 0.029 -.0016366 -.000089 edu_res | .1275808 .0186301 6.85 0.000 .0909616 .1642 _cons | -.1868573 .2835905 -0.66 0.510 -.7442793 .3705647 ------------------------------------------------------------------------------ Note that the coefficients are the same as those from the 2SLS, but Std. Err. values are different. It is advised to use the command, 2SLS, which corrects for Std. Err.