面板数据的个体固定效应模型 - Stata专版

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淡「凡惜 发表于 2014-11-20 15:00:52 |AI写论文

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现在在学stata中面板数据处理这快的东西，想要做一下无固定效应模型、个体固定效应模型、时间固定效应模型和双向固定效应模型想请教一下论坛里的大虾们，无面板数据的无固定效应模型是不是就是随机效应模型啊？
还有就是在做个体固定效应模型的时候，得到的结果如下，看不懂，不知道命令有没有错，麻烦谁能帮我看一下

. xtreg x p nights aincome age education i.id, fe robust
note: 2.id omitted because of collinearity
note: 3.id omitted because of collinearity
note: 4.id omitted because of collinearity
note: 5.id omitted because of collinearity
note: 6.id omitted because of collinearity
note: 7.id omitted because of collinearity
note: 8.id omitted because of collinearity
note: 9.id omitted because of collinearity
note: 10.id omitted because of collinearity
note: 11.id omitted because of collinearity
note: 12.id omitted because of collinearity
note: 13.id omitted because of collinearity
note: 14.id omitted because of collinearity
note: 15.id omitted because of collinearity
note: 16.id omitted because of collinearity
note: 17.id omitted because of collinearity
note: 18.id omitted because of collinearity
note: 19.id omitted because of collinearity
note: 20.id omitted because of collinearity
note: 21.id omitted because of collinearity
note: 22.id omitted because of collinearity
note: 23.id omitted because of collinearity
note: 24.id omitted because of collinearity
note: 25.id omitted because of collinearity
note: 26.id omitted because of collinearity
note: 27.id omitted because of collinearity
note: 28.id omitted because of collinearity
note: 29.id omitted because of collinearity
note: 30.id omitted because of collinearity
note: 31.id omitted because of collinearity
note: 32.id omitted because of collinearity
note: 33.id omitted because of collinearity
note: 34.id omitted because of collinearity
note: 35.id omitted because of collinearity
note: 36.id omitted because of collinearity
note: 37.id omitted because of collinearity
note: 38.id omitted because of collinearity
note: 39.id omitted because of collinearity

Fixed-effects (within) regression             Number of obs    =    312
Group variable: id                            Number of groups =       39

R-sq:  within  = 0.0639                      Obs per group: min =       8
   between = 0.0524                                     avg =    8.0
   overall = 0.0469                                     max =       8

                                             F(5,38)          =    3.15
corr(u_i, Xb)  = 0.1186                      Prob > F          = 0.0178

                                 (Std. Err. adjusted for 39 clusters in id)
------------------------------------------------------------------------------
         |             Robust
         x |    Coef. Std. Err.    t P>|t|    [95% Conf. Interval]
-------------+----------------------------------------------------------------
         p | -.000778 .0010395 -0.75 0.459 -.0028824 .0013263
   nights |  -.0184407 .1534497 -0.12 0.905 -.3290833 .2922019
   aincome | .0035044 .001795    1.95 0.058 -.0001295 .0071382
      age |  -.4237238 .4272794 -0.99 0.328 -1.288706 .4412581
education |  -.9090608 1.153993 -0.79 0.436 -3.245198 1.427076
         |
      id |
      2  |       0  (omitted)
      3  |       0  (omitted)
      4  |       0  (omitted)
      5  |       0  (omitted)
      6  |       0  (omitted)
      7  |       0  (omitted)
      8  |       0  (omitted)
      9  |       0  (omitted)
      10  |       0  (omitted)
      11  |       0  (omitted)
      12  |       0  (omitted)
      13  |       0  (omitted)
      14  |       0  (omitted)
      15  |       0  (omitted)
      16  |       0  (omitted)
      17  |       0  (omitted)
      18  |       0  (omitted)
      19  |       0  (omitted)
      20  |       0  (omitted)
      21  |       0  (omitted)
      22  |       0  (omitted)
      23  |       0  (omitted)
      24  |       0  (omitted)
      25  |       0  (omitted)
      26  |       0  (omitted)
      27  |       0  (omitted)
      28  |       0  (omitted)
      29  |       0  (omitted)
      30  |       0  (omitted)
      31  |       0  (omitted)
      32  |       0  (omitted)
      33  |       0  (omitted)
      34  |       0  (omitted)
      35  |       0  (omitted)
      36  |       0  (omitted)
      37  |       0  (omitted)
      38  |       0  (omitted)
      39  |       0  (omitted)
         |
   _cons | 69.32457 27.17312    2.55 0.015    14.31548 124.3337
-------------+----------------------------------------------------------------
   sigma_u |  28.137014
   sigma_e |  7.0153835
      rho | .9414732 (fraction of variance due to u_i)
------------------------------------------------------------------------------

reg x p nights aincome age education i.id
xtreg x p nights aincome age education i.id, fe robust
这两条命令的区别是什么呢？
还有一个问题就是
xtreg x p nights aincome age education yr*, fe是双向的固定效应模型，那如何做仅仅是时间固定效应模型的回归呢？
望高人解答
.