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Stata 12中的哪个命令可用于解释有限因变量模型的系数?

margins stata

1

CHEBURASHKA · 技术社区 · 12 年前

我正在运行以下代码:

oprobit var1 var2 var3 var4 var5 var2##var3 var4##var5 var6 var7 etc.

如果没有交互项,我本可以使用以下代码来解释系数:

mfx compute, predict(outcome(2))

[结果等于2(我总共有4个结果)]

但从那以后 mfx 不适用于交互项,我得到了一个错误。我试着用 margins 命令,但也不起作用!!! margins var2 var3 var4 var5 var2##var3 var4##var5 var6 var7 etc... , post

利润 仅适用于交互项: (margins var2 var3 var4 var5, post) 我使用什么命令来解释交互和正则变量?

最后,使用简单的语言,我的问题是:给定上面的回归模型,我可以使用什么命令来解释系数?

2 回复 | 直到 12 年前

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dimitriy 12 年前

mfx是一个旧命令,已被页边空白所取代。这就是为什么它不适用于您用来定义交互的因子变量表示法。我不清楚您使用margins命令实际打算计算什么。

以下是一个如何获得结果2概率的平均边际效应的例子:

. webuse fullauto
(Automobile Models)

. oprobit rep77 i.foreign c.weight c.length##c.mpg

Iteration 0:   log likelihood = -89.895098  
Iteration 1:   log likelihood = -76.800575  
Iteration 2:   log likelihood = -76.709641  
Iteration 3:   log likelihood = -76.709553  
Iteration 4:   log likelihood = -76.709553  

Ordered probit regression                         Number of obs   =         66
                                                  LR chi2(5)      =      26.37
                                                  Prob > chi2     =     0.0001
Log likelihood = -76.709553                       Pseudo R2       =     0.1467

--------------------------------------------------------------------------------
         rep77 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     1.foreign |   1.514739   .4497962     3.37   0.001      .633155    2.396324
        weight |  -.0005104   .0005861    -0.87   0.384    -.0016593    .0006384
        length |   .0969601   .0348506     2.78   0.005     .0286542     .165266
           mpg |   .4747249   .2241349     2.12   0.034     .0354286    .9140211
               |
c.length#c.mpg |  -.0020602   .0013145    -1.57   0.117    -.0046366    .0005161
---------------+----------------------------------------------------------------
         /cut1 |   17.21885   5.386033                      6.662419    27.77528
         /cut2 |   18.29469   5.416843                      7.677877    28.91151
         /cut3 |   19.66512   5.463523                      8.956814    30.37343
         /cut4 |   21.12134   5.515901                      10.31038    31.93231
--------------------------------------------------------------------------------

.  margins, dydx(*) predict(outcome(2))

Average marginal effects                          Number of obs   =         66
Model VCE    : OIM

Expression   : Pr(rep77==2), predict(outcome(2))
dy/dx w.r.t. : 1.foreign weight length mpg

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   1.foreign |  -.2002434   .0576487    -3.47   0.001    -.3132327    -.087254
      weight |   .0000828   .0000961     0.86   0.389    -.0001055    .0002711
      length |  -.0088956    .003643    -2.44   0.015    -.0160356   -.0017555
         mpg |   -.012849   .0085546    -1.50   0.133    -.0296157    .0039178
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

如果你想要预测,而不是边际效应,试着

margins, predict(outcome(2))

在非线性模型中,仅相互作用项的边际效应更难计算。细节 here .

2

0

Client Clientovich 12 年前

The marginal effects for positive outcomes, Pr(depvar1=1, depvar2=1), are
        . mfx compute, predict(p11)
The marginal effects for Pr(depvar1=1, depvar2=0) are
        . mfx compute, predict(p10)
The marginal effects for Pr(depvar1=0, depvar2=1) are
        . mfx compute, predict(p01)