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st: Re: multivariate count regression


From   "Scott Merryman" <smerryman@kc.rr.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: multivariate count regression
Date   Thu, 14 Aug 2003 21:04:14 -0500

----- Original Message -----
From: "Ralf Dewenter" <ralf.dewenter@unibw-hamburg.de>
To: <statalist@hsphsun2.harvard.edu>
Sent: Thursday, August 14, 2003 12:44 PM
Subject: st: multivariate count regression


> Hi all,
>
> Does anybody know, where to find an ado-file
> for multivariate count regressions?
> Thanks.
>
> Regards
>
> Ralf Dewenter
>

Would -poisson- or -nbreg- with -suest- be sufficient?  I believe it is similar
to -mvreg- in that it estimates the between-equation covariance so you can test
coefficients across equations.

Example:

. clear

. set obs 100
obs was 0, now 100

. gen x1 = invnorm(uniform())

. gen x2 = invnorm(uniform())

. gen xb = 1 + 2*x1 - x2

. genpoisson y, xbeta(xb)

. genpoisson y2, xbeta(xb)

. qui poisson y x1 x2,score(s1)

. est store A

. qui poisson y2 x1 x2, score(s2)

. est store B

. suest A B

Simultaneous results for A, B
                                                            Obs      =     100

------------------------------------------------------------------------------
             |               Robust
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
A_y          |
          x1 |   1.432079   .1199582    11.94   0.000     1.196966    1.667193
          x2 |  -.7417589   .0804557    -9.22   0.000    -.8994491   -.5840686
       _cons |    1.45254   .0961629    15.10   0.000     1.264064    1.641015
-------------+----------------------------------------------------------------
B_y2         |
          x1 |   1.418806   .1223085    11.60   0.000     1.179085    1.658526
          x2 |  -.7914187   .0901748    -8.78   0.000     -.968158   -.6146794
       _cons |   1.459112    .097084    15.03   0.000      1.26883    1.649393
------------------------------------------------------------------------------

. test [A_y]x2 = [B_y2]x2, common

 ( 1)  [A_y]x2 - [B_y2]x2 = 0

           chi2(  1) =    1.48
         Prob > chi2 =    0.2236


Scott


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