关于行业虚拟变量~~着急

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收藏 2011-09-23

有21个行业, 设置了 20个行业虚拟变量, 为什么stata 在回归结果中, 显示因共线删除了3个行业虚拟变量,
还有为什么当选择不同的基准行业(比如一次是将行业1作为基准回归 , 另一次是将行业2作为基准回归). 解释变量的系数会有变化,甚至符号都变了~~

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蓝色

2011-9-23 08:03:52

把结果贴出来

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han1234567

2011-9-23 08:32:02

note: dum_in19 omitted because of collinearity
note: dum_in20 omitted because of collinearity
Iteration 0: log pseudolikelihood = -1398.6116
Iteration 1: log pseudolikelihood = -1343.6895
Iteration 2: log pseudolikelihood = -1343.2777
Iteration 3: log pseudolikelihood = -1343.2769
Iteration 4: log pseudolikelihood = -1343.2769

Probit regression                               Number of obs =    2033
                                                Wald chi2(20) =    103.78
                                                Prob > chi2    =    0.0000
Log pseudolikelihood = -1343.2769                Pseudo R2    =    0.0396

------------------------------------------------------------------------------
         |             Robust
   pin_d |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
      IM | .0349286 .0152563    2.29 0.022    .0050268 .0648305
      EX |  -.0012979 .0094316 -0.14 0.891 -.0197835 .0171878
   dum_in1 | .3935336 .2281809    1.72 0.085 -.0536927 .8407599
   dum_in2 |  -.1814786 .1931936 -0.94 0.348 -.5601311 .1971739
   dum_in3 |  -.8520875 .2908348 -2.93 0.003 -1.422113 -.2820617
   dum_in4 |  -.3617536 .3291265 -1.10 0.272    -1.00683 .2833224
   dum_in5 | -.30975 .371427 -0.83 0.404 -1.037734 .4182336
   dum_in6 | .023996 .2554818    0.09 0.925 -.4767391 .5247311
   dum_in7 |  -.7105277 .2728539 -2.60 0.009 -1.245312 -.1757438
   dum_in8 | -.814368 .4685178 -1.74 0.082 -1.732646    .10391
   dum_in9 | .6191057 .159261    3.89 0.000    .3069599 .9312515
dum_in10 |  -.1450727 .1555843 -0.93 0.351 -.4500123 .1598668
dum_in11 |  -.8040283 .2403151 -3.35 0.001 -1.275037 -.3330194
dum_in12 |  -.0513121 .1577078 -0.33 0.745 -.3604136 .2577895
dum_in13 |  -.4206497 .1749261 -2.40 0.016 -.7634986 -.0778008
dum_in14 | .1663208 .1108897    1.50 0.134    -.051019 .3836606
dum_in15 | .3416866 .3147164    1.09 0.278 -.2751462 .9585193
dum_in16 |  -.0610019 .1786916 -0.34 0.733    -.411231 .2892272
dum_in17 | .1709796 .1179171    1.45 0.147 -.0601337 .4020929
dum_in18 | .7371363 .1860433    3.96 0.000    .3724982 1.101774
dum_in19 |  (omitted)
dum_in20 |  (omitted)
   _cons |  -.4243642 .2272548 -1.87 0.062 -.8697753    .021047
------------------------------------------------------------------------------

.设置行业21 为基组.  回归结果中自动删除共线的行业19 行业20 虚拟变量 . 基础不好也不知道该怎么办~~搞得自己很郁闷的说.

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han1234567

2011-9-23 08:33:45

note: dum_in20 omitted because of collinearity
note: dum_in21 omitted because of collinearity
Iteration 0: log pseudolikelihood = -1398.6116
Iteration 1: log pseudolikelihood = -1343.6895
Iteration 2: log pseudolikelihood = -1343.2777
Iteration 3: log pseudolikelihood = -1343.2769
Iteration 4: log pseudolikelihood = -1343.2769

Probit regression                               Number of obs =    2033
                                                Wald chi2(20) =    103.78
                                                Prob > chi2    =    0.0000
Log pseudolikelihood = -1343.2769                Pseudo R2    =    0.0396

------------------------------------------------------------------------------
         |             Robust
   pin_d |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
      IM | .0311916 .0143081    2.18 0.029    .0031483 .0592348
      EX | -.091106 .0454971 -2.00 0.045 -.1802787 -.0019334
   dum_in2 | 1.078482 .710958    1.52 0.129 -.3149699 2.471934
   dum_in3 |  -.4968943 .3425986 -1.45 0.147 -1.168375 .1745866
   dum_in4 |  -.1457143 .3649781 -0.40 0.690 -.8610581 .5696296
   dum_in5 | -.575388 .3103894 -1.85 0.064    -1.18374 .0329639
   dum_in6 | 2.73976 1.532796    1.79 0.074 -.2644648 5.743984
   dum_in7 | 1.734472 1.405297    1.23 0.217 -1.019861 4.488804
   dum_in8 | .7420026 .8277599    0.90 0.370    -.880377 2.364382
   dum_in9 | 1.186823 .4467537    2.66 0.008    .3112016 2.062444
dum_in10 | .3757353 .3958568    0.95 0.343 -.4001299    1.1516
dum_in11 |  -.2984878 .3593642 -0.83 0.406 -1.002829    .405853
dum_in12 | 1.041101 .6935215    1.50 0.133 -.3181764 2.400378
dum_in13 | .3855754 .4840658    0.80 0.426    -.563176 1.334327
dum_in14 | 1.31457 .6990983    1.88 0.060 -.0556377 2.684777
dum_in15 | 1.388521 .6540812    2.12 0.034    .1065451 2.670496
dum_in16 | .2419258 .2665698    0.91 0.364 -.2805414    .764393
dum_in17 | 1.571164 .8130696    1.93 0.053 -.0224234 3.164751
dum_in18 | 1.861136 .7207002    2.58 0.010    .4485895 3.273682
dum_in19 | 1.564181 .9069524    1.72 0.085    -.213413 3.341775
dum_in20 |  (omitted)
dum_in21 |  (omitted)
   _cons | -.041686 .1289653 -0.32 0.747 -.2944532 .2110813
------------------------------------------------------------------------------

. 将行业1 为基组时 ( 因变量是firm-level 变量. 自变量是industry-level 的进出口竞争, 所以我想加入行业变量来控制 fixed industry effect)

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han1234567

2011-9-23 08:43:07

note: dum_in19 omitted because of collinearity
note: dum_in20 omitted because of collinearity
note: dum_in21 omitted because of collinearity
Iteration 0: log pseudolikelihood = -1398.6116
Iteration 1: log pseudolikelihood = -1343.6895
Iteration 2: log pseudolikelihood = -1343.2777
Iteration 3: log pseudolikelihood = -1343.2769
Iteration 4: log pseudolikelihood = -1343.2769

Probit regression                               Number of obs =    2033
                                                Wald chi2(20) =    103.78
                                                Prob > chi2    =    0.0000
Log pseudolikelihood = -1343.2769                Pseudo R2    =    0.0396

------------------------------------------------------------------------------
         |             Robust
   pin_d |    Coef. Std. Err.    z P>|z|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
      IM | .0349286 .0152563    2.29 0.022    .0050268 .0648305
      EX |  -.0012979 .0094316 -0.14 0.891 -.0197835 .0171878
   dum_in1 | .3935336 .2281809    1.72 0.085 -.0536927 .8407599
   dum_in2 |  -.1814786 .1931936 -0.94 0.348 -.5601311 .1971739
   dum_in3 |  -.8520875 .2908348 -2.93 0.003 -1.422113 -.2820617
   dum_in4 |  -.3617536 .3291265 -1.10 0.272    -1.00683 .2833224
   dum_in5 | -.30975 .371427 -0.83 0.404 -1.037734 .4182336
   dum_in6 | .023996 .2554818    0.09 0.925 -.4767391 .5247311
   dum_in7 |  -.7105277 .2728539 -2.60 0.009 -1.245312 -.1757438
   dum_in8 | -.814368 .4685178 -1.74 0.082 -1.732646    .10391
   dum_in9 | .6191057 .159261    3.89 0.000    .3069599 .9312515
dum_in10 |  -.1450727 .1555843 -0.93 0.351 -.4500123 .1598668
dum_in11 |  -.8040283 .2403151 -3.35 0.001 -1.275037 -.3330194
dum_in12 |  -.0513121 .1577078 -0.33 0.745 -.3604136 .2577895
dum_in13 |  -.4206497 .1749261 -2.40 0.016 -.7634986 -.0778008
dum_in14 | .1663208 .1108897    1.50 0.134    -.051019 .3836606
dum_in15 | .3416866 .3147164    1.09 0.278 -.2751462 .9585193
dum_in16 |  -.0610019 .1786916 -0.34 0.733    -.411231 .2892272
dum_in17 | .1709796 .1179171    1.45 0.147 -.0601337 .4020929
dum_in18 | .7371363 .1860433    3.96 0.000    .3724982 1.101774
dum_in19 |  (omitted)
dum_in20 |  (omitted)
dum_in21 |  (omitted)
   _cons |  -.4243642 .2272548 -1.87 0.062 -.8697753    .021047
------------------------------------------------------------------------------

.
这个结果是设置21个行业虚拟变量, 并保留截距,  stata 自动删除三个行业虚拟变量的结果.