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Re: st: st: Interpreting mfx & margeff Coefficients from GLM


From   Richard Williams <richardwilliams.ndu@gmail.com>
To   statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu
Subject   Re: st: st: Interpreting mfx & margeff Coefficients from GLM
Date   Tue, 03 Apr 2012 09:29:59 -0500

At 04:01 PM 4/2/2012, Clifton Chow wrote:
Stata listers,

I ran a glm log link (family gamma from modified park's test) on my dataset in which the dependent variables is hours worked. To interpret the coefficients in the model, I transformed it via mfx and margeff. I have a couple of questions about interpreting the coefficients after transformation;

1. Is mfx mainly appropriate for indicator explanatory variables and margeff for continous ones? 2. Can I interpret the results of the mfx coefficients in the raw scale (i.e. hours)?
3. What does the column in the far right X referred to in the mfx output?

Most people are going to want to use the -margins- command rather than the older -mfx- or -margeff-. The far right column in -mfx- is the mean of the X variable. I am not sure what is happening with this particular combo of link and family, but an intro to -margins- is at http://www.nd.edu/~rwilliam/stats/Margins01.pdf .

Below is my partial mfx output from GLM:

     y  = Predicted mean hours (predict)
         =  8.7005381
------------------------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------------------------
X1*| 8.891796 1.83527 4.84 0.000 5.29474 12.4889 .237385 X2*| -5.151794 1.41499 -3.64 0.000 -7.92513 -2.37846 .519545
(*) dy/dx is for discrete change of dummy variable from 0 to 1

4. For margeff, to interpret these in hours, should I exp(margeff coefficient)?


Average partial effects after glm
      y  = log(hours)

------------------------------------------------------------------------------
variable | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X1 | 16.5519 3.9611 4.18 0.000 8.788287 24.31551
 X2         |  -7.379914   1.553438    -4.75   0.000     -10.4246   -4.335232


Thanks
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Richard Williams, Notre Dame Dept of Sociology
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