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st: seemingly unrelated regression


From   "James Unnever" <unnever@sar.usf.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: seemingly unrelated regression
Date   Sat, 27 Jun 2009 17:31:39 -0400

I just got a journal review back and a reviewer suggested that we use SUR.
I have never used this procedure and know nothing about it.  However, I do
not think it is appropriate.  I plan on sending the below to the editor.
Could someone please tell me whether it is correct?

Seemingly unrelated regression should be used when researchers run multiple
models on the same set of observations thus generating the possibility that
the residual errors from each equation may be correlated.  It would be
appropriate to use this procedure (in SAS , Proc Syslin with the SUR option)
if, for example, we were analyzing a dataset like the GSS that had multiple
measures of punitive attitudes (support for the death penalty-should local
courts be more harsh) and where running similar equations with each
dependent variables (the equations cannot be identical).  In this case, the
residual errors from each equation could be correlated and thus this
procedure would be appropriate. 

 
This is not the case for our cross-national data.  Each of our regression
runs are computed on a different data set -different observations.  Each
dataset was independently collected in the countries we analyzed.  It may
appear that the same observations are being analyzed because the same
questions were asked, after the codebook was reinterpreted, in each country.
However, each country has a unique identifier and an "if" statement must be
included (e.g., if country = "England") to subset the data for that
particular country.  

Thanks,

Jim




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