  # st: Re: Seemingly unrelated regression or similar estimation

 From "Scott Merryman" To Subject st: Re: Seemingly unrelated regression or similar estimation Date Tue, 23 Sep 2003 21:17:46 -0500

```----- Original Message -----
From: "Ed Levitas" <levitas@uwm.edu>
To: <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, September 23, 2003 9:25 AM
Subject: st: Seemingly unrelated regression or similar estimation

> Statalisters,
>
> I have two equations:
>
> F = B(0) + B(1)var1 + B(2)var2 + U(1)
> G = Y(0) + Y(1)var1 + Y(2)var2 + U(2)
>
> F is continuous and G is a positive integer.  Are there ways I can perform
> joint tests of coefficients across the two equations (e.g. B(1) = Y(1) = 0).
> This seems like a Seemingly Unrelated Regression.  But given the difference
> in error distributions, I would think some major modifications are in order.
> Can anyone suggest recommendations (e.g. ado files), citations, etc.
>
>
> -Ed
>

How about Seeming Unrelated Estimation -suest-

Example:

. use "C:\Stata8\auto.dta", clear
(1978 Automobile Data)

. qui poisson rep mpg weight for, score(A)

. est store poisson

. qui reg price mpg weight for, score(B)

. est store OLS

. suest poisson OLS

Simultaneous results for poisson, OLS
Obs      =      74

------------------------------------------------------------------------------
|               Robust
|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
poisson_r~78 |
mpg |   .0143303    .005316     2.70   0.007     .0039112    .0247494
weight |   .0000841   .0000604     1.39   0.164    -.0000343    .0002025
foreign |   .3581404    .095116     3.77   0.000     .1717164    .5445644
_cons |   .5416934   .2650292     2.04   0.041     .0222457    1.061141
-------------+----------------------------------------------------------------
OLS_mean     |
mpg |    21.8536   79.07015     0.28   0.782    -133.1211    176.8283
weight |   3.464706   .7614705     4.55   0.000     1.972251    4.957161
foreign |    3673.06   651.1297     5.64   0.000      2396.87    4949.251
_cons |  -5853.696    3793.29    -1.54   0.123    -13288.41    1581.017
-------------+----------------------------------------------------------------
OLS_lnvar    |
_cons |   15.32848   .2000052    76.64   0.000     14.93647    15.72048
------------------------------------------------------------------------------

. test [poisson_rep78]mpg =0

( 1)  [poisson_rep78]mpg = 0

chi2(  1) =    7.27
Prob > chi2 =    0.0070

. test [OLS_mean]mpg =0, accum

( 1)  [poisson_rep78]mpg = 0
( 2)  [OLS_mean]mpg = 0

chi2(  2) =    7.35
Prob > chi2 =    0.0253

Hope this helps,
Scott

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