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Re: st: FGLS-SUR model estimation in STATA

From   Kyle Hood <>
Subject   Re: st: FGLS-SUR model estimation in STATA
Date   Thu, 7 Dec 2006 11:35:51 -0500 (EST)

GLS is a least squares procedure in which the variance-covariance matrix of the residuals is not proportional to the identity matrix. My interpretation is that FGLS refers to any estimation procedure in which an initial procedure is used to generate a consistent estimate of the error covariance matrix, which is then plugged in to a GLS procedure. Because of this, I believe that any SUR procedure that does not ask you to somehow input information about the error covariance matrix must estimate this matrix itself, and so has to performing FGLS.


On Thu, 7 Dec 2006, Jeremy Whitridge Cheesman wrote:


I have a cross-sectional dataset that pools multiple observations
household volumetric demand for piped versus well water given changes in
piped and well water prices. I have multiple observations for each
household and different numbers of observations between households. I have
socio-economic information for each household that is invariant across
observations. I want to estimate a system of demand equations for both
private and well water controlling for error term correlation across the
equations and using the water prices and socio-economic data as
explanatory variabels.

Its my first time dealing with this sort of data. My understanding is that
I need to implement a Seemingly Unrelated Regression model with FGLS to
get efficient estimators. I have found how to implement SUR in STATA but I
donít know whether this is a FGLS estimation or not. Can someone help? If
SUR is not FGLS, how do I go about implementing it in STATA?


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