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Re: st: beginnerXs ask about Xtlogit probabilities


From   "Clive Nicholas" <Clive.Nicholas@newcastle.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: beginnerXs ask about Xtlogit probabilities
Date   Sat, 15 Nov 2003 20:40:14 -0000 (GMT)

Hmmmm, are you *sure*, Scott? As I understand it, -listcoef- doesn't work
with -xtlogit- (or -xtprobit-, for that matter: I checked the other day).
I'll be delighted if I'm wrong, but...

...by the way, thanks a lot for -margin-. You're right: once the
regression is run, it really *does* run much more more quickly than -mfx
compute- (and then some!). I wish I knew about that program much earlier.

I do share Nick Varian's concerns about insignificant marginal effects as
against *significant* regression coefficients. In running -margin- after
one model (which contained 16 significant regressors; pseudo-R^2: 0.444),
not one of them had significant marginal effects. That does seem very
strange!

C.

> ----- Original Message -----
> From: "Nick Varian" <nk7_br@yahoo.com.br>
> To: <statalist@hsphsun2.harvard.edu>
> Sent: Friday, November 14, 2003 5:51 AM
> Subject: Re: st: beginnerXs ask about Xtlogit probabilities
>
>
>> Scott,
>> thanks for your help. Could you help me once more? I
>> am a lit bite confused, because using xtlogit i got
>> some p-value that means that may parameter is
>> significant different from zero. After calculing mfx
>> compute, they all turn to insignificant. What does its
>> means? In which one should I believe?
>>
>>
>
> I can't really help you with that, however you may find reporting the
> percentage
> change in odds rather than the marginal effects to be insightful.
> -listcoef-
> will conveniently provide you with the odds ratio and the percentage
> change in
> odds.
>
> For example:
>
> . webuse union
> (NLS Women 14-24 in 1968)
>
> . xtlogit union age grade south year, i(id) fe nolog
> note: multiple positive outcomes within groups encountered.
> note: 2744 groups (14165 obs) dropped due to all positive or
>       all negative outcomes.
>
> Conditional fixed-effects logistic regression   Number of obs      =
> 12035
> Group variable (i): idcode                      Number of groups   =
> 1690
>
>                                                 Obs per group: min =
>   2
>                                                                avg =
> 7.1
>                                                                max =
>  12
>
>                                                 LR chi2(4)         =
> 68.46
> Log likelihood  = -4515.9536                    Prob > chi2        =
> 0.0000
>
> ------------------------------------------------------------------------------
>        union |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
>          age |   .0758677   .0960711     0.79   0.430    -.1124282
> .2641637
>        grade |   .0857237   .0418685     2.05   0.041     .0036629
> .1677845
>        south |  -.7469976   .1249048    -5.98   0.000    -.9918065
> -.5021887
>         year |   -.059335   .0967972    -0.61   0.540     -.249054
> .1303839
> ------------------------------------------------------------------------------
>
> . mfx compute, predict(pu0)
>
> Marginal effects after clogit
>       y  = Pr(union|fixed effect is 0) (predict, pu0)
>          =  .16861097
> ------------------------------------------------------------------------------
> variable |      dy/dx    Std. Err.     z    P>|z|  [    95% C.I.   ]
> X
> ---------+--------------------------------------------------------------------
>      age |   .0106352      .02044    0.52   0.603  -.029435  .050705
> 30.538
>    grade |   .0120169      .03911    0.31   0.759  -.064646  .088679
> 12.7934
>    south*|   -.099099      .32063   -0.31   0.757  -.727525  .529327
> .381388
>     year |  -.0083177      .01301   -0.64   0.522  -.033809  .017174
> 79.6184
> ------------------------------------------------------------------------------
> (*) dy/dx is for discrete change of dummy variable from  to 1
>
> . listcoef, p
>
> clogit (N=12035): Percentage Change in Odds
>
>   Odds of: 1 vs 0
>
> --------------------------------------------------
>        union |      b         z     P>|z|      %
> -------------+------------------------------------
>          age |   0.07587    0.790   0.430      7.9
>        grade |   0.08572    2.047   0.041      9.0
>        south |  -0.74700   -5.981   0.000    -52.6
>         year |  -0.05934   -0.613   0.540     -5.8
> --------------------------------------------------
>
> With this, the interpretation is
>     For each additional grade the odds of being in a union increase by 9%
> holding     all other variables constant.
>
> or,
>     Working in the south reduces the odds of being in a union by 53%
>
> Hope this helps,
> Scott
>
>
> *
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>


Yours,
CLIVE NICHOLAS,
Politics Building,
School of Geography, Politics and Sociology,
University of Newcastle-upon-Tyne,
Newcastle-upon-Tyne,
NE1 7RU,
United Kingdom.
*
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*   http://www.stata.com/support/statalist/faq
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