Hi,

I ran the following command on my panel data:

Code:
xtlogit A B C D E F G logH
And got the following result:

PHP Code:
       A |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        B |  -.0106103   .0016808    -6.31   0.000    -.0139045    -.007316
        C |  -.0250802   .0105435    -2.38   0.017    -.0457451   -.0044154
        D |  -.6328279   .1144142    -5.53   0.000    -.8570757   -.4085802
        E |  -.0107726   .0018946    -5.69   0.000    -.0144859   -.0070592
        F |  -.0005993   .0029046    -0.21   0.837    -.0062923    .0050937
        G |   .0024517   .0038712     0.63   0.527    -.0051358    .0100391
     logH |   .9919885   .2078777     4.77   0.000     .5845557    1.399421
    _cons |  -17.61147    1.14188   -15.42   0.000    -19.84952   -15.37343
----------------------+---------------------------------------------------------------- 
As I would like to interpret the results, I used the following command for marginal effects:

Code:
margins, dydx(*) predict(pu0) nose
Nose was used to save time as it is a big dataset, will compute it during the night for standard errors. This is the result:

PHP Code:
----------------------------------------------------------------------------------
          |      dy/dx
----------------------+----------------------------------------------------------------
        A |  -5.94e-06
        B |   -.000014
        C |  -.0003541
        D |  -6.03e-06
        E |  -3.35e-07
        F |   1.37e-06
     logH |   .0005551
--------------------------------------------------------------------------------------- 

Due to the scientific notation of the coefficients, I am not confident in how to interpret my results. I have a preference towards using margins at the mean value of the variable, but using the code mfx, at(mean) nose does not give the needed result as this is related to a linear prediction. To me it would make more sense to use marginal effects in combination with the mean of the variable.

Is there a way to make more sense of the marginal effects and a possibility to include mean values? I've looked at many topics on the forum and watched several videos on YouTube, but so far I was unable to find a question for my answer.

Thank you in advance,

Django