按照这个命令：reg lnc lny pension gender age ms education people health sd lnr lnv lnia loan assistance i.id<br>
出现的结果：id:  factor variables may not contain noninteger values<br>
按照这个命令：xi:reg lnc lny pension gender age ms education people health sd lnr lnv lnia loan assistance i.id<br>
出现的结果：<br>
i.id              _Iid_1-1411         (_Iid_1 for id==.1000000014901161 omitted)note: _Iid_61 omitted because of collinearitynote: _Iid_65 omitted because of collinearitynote: _Iid_70 omitted because of collinearitynote: _Iid_84 omitted because of collinearitynote: _Iid_88 omitted because of collinearitynote: _Iid_91 omitted because of collinearitynote: _Iid_95 omitted because of collinearitynote: _Iid_117 omitted because of collinearitynote: _Iid_128 omitted because of collinearitynote: _Iid_177 omitted because of collinearitynote: _Iid_208 omitted because of collinearitynote: _Iid_212 omitted because of collinearitynote: _Iid_238 omitted because of collinearitynote: _Iid_274 omitted because of collinearitynote: _Iid_312 omitted because of collinearitynote: _Iid_370 omitted because of collinearitynote: _Iid_388 omitted because of collinearitynote: _Iid_430 omitted because of collinearitynote: _Iid_436 omitted because of collinearitynote: _Iid_443 omitted because of collinearitynote: _Iid_467 omitted because of collinearitynote: _Iid_482 omitted because of collinearitynote: _Iid_493 omitted because of collinearitynote: _Iid_499 omitted because of collinearitynote: _Iid_528 omitted because of collinearitynote: _Iid_530 omitted because of collinearitynote: _Iid_536 omitted because of collinearitynote: _Iid_540 omitted because of collinearitynote: _Iid_553 omitted because of collinearitynote: _Iid_568 omitted because of collinearitynote: _Iid_609 omitted because of collinearitynote: _Iid_614 omitted because of collinearitynote: _Iid_620 omitted because of collinearitynote: _Iid_626 omitted because of collinearitynote: _Iid_651 omitted because of collinearitynote: _Iid_655 omitted because of collinearitynote: _Iid_676 omitted because of collinearitynote: _Iid_677 omitted because of collinearitynote: _Iid_693 omitted because of collinearitynote: _Iid_702 omitted because of collinearitynote: _Iid_725 omitted because of collinearitynote: _Iid_740 omitted because of collinearitynote: _Iid_765 omitted because of collinearitynote: _Iid_774 omitted because of collinearitynote: _Iid_793 omitted because of collinearitynote: _Iid_812 omitted because of collinearitynote: _Iid_817 omitted because of collinearitynote: _Iid_838 omitted because of collinearitynote: _Iid_840 omitted because of collinearitynote: _Iid_879 omitted because of collinearitynote: _Iid_907 omitted because of collinearitynote: _Iid_908 omitted because of collinearitynote: _Iid_921 omitted because of collinearitynote: _Iid_924 omitted because of collinearitynote: _Iid_937 omitted because of collinearitynote: _Iid_943 omitted because of collinearitynote: _Iid_965 omitted because of collinearitynote: _Iid_993 omitted because of collinearitynote: _Iid_1013 omitted because of collinearitynote: _Iid_1019 omitted because of collinearitynote: _Iid_1032 omitted because of collinearitynote: _Iid_1038 omitted because of collinearitynote: _Iid_1041 omitted because of collinearitynote: _Iid_1044 omitted because of collinearitynote: _Iid_1045 omitted because of collinearitynote: _Iid_1066 omitted because of collinearitynote: _Iid_1072 omitted because of collinearitynote: _Iid_1101 omitted because of collinearitynote: _Iid_1103 omitted because of collinearitynote: _Iid_1108 omitted because of collinearitynote: _Iid_1126 omitted because of collinearitynote: _Iid_1143 omitted because of collinearitynote: _Iid_1176 omitted because of collinearitynote: _Iid_1191 omitted because of collinearitynote: _Iid_1192 omitted because of collinearitynote: _Iid_1223 omitted because of collinearitynote: _Iid_1231 omitted because of collinearitynote: _Iid_1339 omitted because of collinearitynote: _Iid_1370 omitted because of collinearitynote: _Iid_1383 omitted because of collinearity      Source |       SS           df       MS      Number of obs   =     1,345-------------+----------------------------------   F(1344, 0)      =         .       Model |  1276.63664     1,344   .94987845   Prob &gt; F        =         .    Residual |           0         0           .   R-squared       =    1.0000-------------+----------------------------------   Adj R-squared   =         .       Total |  1276.63664     1,344   .94987845   Root MSE        =         0------------------------------------------------------------------------------         lnc |      Coef.   Std. Err.      t    P&gt;|t|     [95% Conf. Interval]-------------+----------------------------------------------------------------         lny |   .0675591          .        .       .            .           .     pension |   .1573239          .        .       .            .           .      gender |   .3433056          .        .       .            .           .         age |   .0309434          .        .       .            .           .          ms |  -.1769194          .        .       .            .           .   education |   .0534946          .   

做个体固定效应，首先你得是面板数据啊，截面数据做不了啊，做ols吧

哦哦，好的，个体固定效应不能是面板数据是吗

什么跟什么啊，我是说截面数据做不了个体固定效应，什么时候说过面板数据做不了固定效应

横截面数据加一个虚拟变量i.id再回归不行吗？这不是个体固定效应？