Variance-weighted least-squares regression Number of obs = 31Goodness-of-fit chi2(28) = 73.28 Model chi2(2) = 263.97Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 lnY Coef. Std. Err. z P>|z| [95% Conf. Interval] lnX1 .3177322 .0514579 6.17 0.000 .2168765 .4185879 lnX2 .428669 .0275805 15.54 0.000 .3746122 .4827257 _cons 2.338164 .4472981 5.23 0.000 1.461476 3.214852
例4.21（老师有标准答案）
reg Y X
Source SS df MS Number of obs = 29 F( 1, 27) = 2214.60 Model 2.4819e+09 1 2.4819e+09 Prob > F = 0.0000 Residual 30259023.9 27 1120704.59 R-squared = 0.9880 Adj R-squared = 0.9875 Total 2.5122e+09 28 89720219.8 Root MSE = 1058.6 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .4375268 .0092973 47.06 0.000 .4184503 .4566033 _cons 2091.295 334.987 6.24 0.000 1403.959 2778.632
predict e,resid tsset year
time variable: year, 1978 to 2006 delta: 1 unit
line e year,title(\残差相关图\
scatter e e1,title(\残差相关图\
g T=_n g T2=T^2 reg Y X T2
Source SS df MS Number of obs = 29 F( 2, 26) = 5380.77 Model 2.5061e+09 2 1.2531e+09 Prob > F = 0.0000 Residual 6054792.7 26 232876.642 R-squared = 0.9976 Adj R-squared = 0.9974 Total 2.5122e+09 28 89720219.8 Root MSE = 482.57 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .1761519 .0259858 6.78 0.000 .1227374 .2295664 T2 21.65582 2.124183 10.19 0.000 17.2895 26.02215 _cons 3328.191 195.0326 17.06 0.000 2927.296 3729.086 reg e X T2 e1
Source SS df MS Number of obs = 28 F( 3, 24) = 64.94 Model 25597419.6 3 8532473.19 Prob > F = 0.0000 Residual 3153351.72 24 131389.655 R-squared = 0.8903 Adj R-squared = 0.8766 Total 28750771.3 27 1064843.38 Root MSE = 362.48 e Coef. Std. Err. t P>|t| [95% Conf. Interval] X -.1435191 .0335797 -4.27 0.000 -.2128242 -.074214 T2 11.04582 2.915754 3.79 0.001 5.028004 17.06365 e1 .6186482 .1467037 4.22 0.000 .3158666 .9214297 _cons 910.3409 172.739 5.27 0.000 553.8251 1266.857
g e2=e[_n-1] reg e X T2 e1 e2
Source SS df MS Number of obs = 28 F( 4, 23) = 46.69 Model 25598535.3 4 6399633.84 Prob > F = 0.0000 Residual 3152235.94 23 137053.737 R-squared = 0.8904 Adj R-squared = 0.8713 Total 28750771.3 27 1064843.38 Root MSE = 370.21 e Coef. Std. Err. t P>|t| [95% Conf. Interval] X -.1421776 .0373799 -3.80 0.001 -.2195039 -.0648513 T2 10.80845 3.973581 2.72 0.012 2.58847 19.02843 e1 .6192203 .1499666 4.13 0.000 .3089908 .9294498 e2 4.183503 46.36562 0.09 0.929 -91.73108 100.0981 _cons 886.1107 321.3096 2.76 0.011 221.4311 1550.79
prais Y X T2,rhotype(orrc)
Prais-Winsten AR(1) regression -- iterated estimates Source SS df MS Number of obs = 29 F( 2, 26) = 1153.30 Model 215943215 2 107971607 Prob > F = 0.0000 Residual 2434113.93 26 93619.7664 R-squared = 0.9889 Adj R-squared = 0.9880 Total 218377329 28 7799190.31 Root MSE = 305.97 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .1896298 .0292979 6.47 0.000 .1294071 .2498524 T2 20.79527 2.693162 7.72 0.000 15.25939 26.33114 _cons 3118.169 329.4324 9.47 0.000 2441.011 3795.327 rho .764553 Durbin-Watson statistic (original) 0.442033Durbin-Watson statistic (transformed) 1.361658
newey lnY lnX, lag(2)
例4.3.1（P140）
g lnX1=ln(X1) g lnX2=ln(X2) g lnX3=ln(X3) g lnX4=ln(X4) g lnX5=ln(X5) g lnY=ln(Y)
reg lnY lnX1 lnX2 lnX3 lnX4 lnX5
Source SS df MS Number of obs = 25 F( 5, 19) = 202.68 Model .205495866 5 .041099173 Prob > F = 0.0000 Residual .003852744 19 .000202776 R-squared = 0.9816 Adj R-squared = 0.9768 Total .209348611 24 .008722859 Root MSE = .01424 lnY Coef. Std. Err. t P>|t| [95% Conf. Interval] lnX1 .3811446 .050242 7.59 0.000 .275987 .4863022 lnX2 1.222289 .1351786 9.04 0.000 .9393566 1.505221 lnX3 -.0811099 .0153037 -5.30 0.000 -.1131409 -.0490789 lnX4 -.0472287 .0447674 -1.05 0.305 -.1409279 .0464705 lnX5 -.1011737 .0576866 -1.75 0.096 -.2219131 .0195656 _cons -4.173174 1.923624 -2.17 0.043 -8.199365 -.1469838
corr lnX1 lnX2 lnX3 lnX4 lnX5
lnX1 lnX2 lnX3 lnX4 lnX5 lnX1 1.0000 lnX2 -0.5687 1.0000 lnX3 0.4517 -0.2141 1.0000 lnX4 0.9644 -0.6976 0.3988 1.0000 lnX5 0.4402 -0.0733 0.4113 0.2795 1.0000
stepwise, pr(0.05) : reg Y X1 X2 X3 X4 X5
或者stepwise, pe(0.05) : reg Y X1 X2 X3 X4 X5（逐步向前回归和逐步向后回归）
reg lnY lnX1 lnX2 lnX3
Source SS df MS Number of obs = 25 F( 3, 21) = 320.34 Model .204871849 3 .068290616 Prob > F = 0.0000 Residual .004476761 21 .000213179 R-squared = 0.9786 Adj R-squared = 0.9756 Total .209348611 24 .008722859 Root MSE = .0146 lnY Coef. Std. Err. t P>|t| [95% Conf. Interval] lnX1 .3233849 .0108608 29.78 0.000 .3007987 .3459711 lnX2 1.290729 .0961534 13.42 0.000 1.090767 1.490691 lnX3 -.0867539 .0151549 -5.72 0.000 -.1182702 -.0552376 _cons -5.999638 1.162078 -5.16 0.000 -8.416312 -3.582964
例4.4．1（P151）
reg X1 X2 Z
Source SS df MS Number of obs = 31 F( 2, 28) = 1947.55 Model 323280649 2 161640324 Prob > F = 0.0000 Residual 2323912.12 28 82996.8616 R-squared = 0.9929 Adj R-squared = 0.9924 Total 325604561 30 10853485.4 Root MSE = 288.09 X1 Coef. Std. Err. t P>|t| [95% Conf. Interval] X2 -.470904 .1154633 -4.08 0.000 -.7074199 -.2343881 Z 1.460539 .0860022 16.98 0.000 1.284372 1.636707 _cons 132.7416 194.2843 0.68 0.500 -265.2317 530.7149
predict v,resid reg Y X1 X2 v
Source SS df MS Number of obs = 31 F( 3, 27) = 1313.48 Model 169977392 3 56659130.6 Prob > F = 0.0000 Residual 1164688.99 27 43136.6292 R-squared = 0.9932 Adj R-squared = 0.9924 Total 171142081 30 5704736.02 Root MSE = 207.69 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X1 .4502363 .042451 10.61 0.000 .3631339 .5373386 X2 .4025897 .0638268 6.31 0.000 .2716278 .5335515 v 1.191137 .1427031 8.35 0.000 .8983341 1.483939 _cons 155.6975 140.1522 1.11 0.276 -131.871 443.266
ivreg Y X2 (X1=Z)
Instrumental variables (2SLS) regression Source SS df MS Number of obs = 31 F( 2, 28) = 513.69 Model 166680210 2 83340105 Prob > F = 0.0000 Residual 4461870.66 28 159352.524 R-squared = 0.9739 Adj R-squared = 0.9721 Total 171142081 30 5704736.02 Root MSE = 399.19 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X1 .4502363 .0815915 5.52 0.000 .2831037 .6173688 X2 .4025897 .122676 3.28 0.003 .1512992 .6538801 _cons 155.6975 269.3743 0.58 0.568 -396.0907 707.4858
reg Y X1 X2