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RE: st: xtivreg2 - Incomplete computation of first stage regression
From
DE SOUZA Eric <[email protected]>
To
"'[email protected]'" <[email protected]>
Subject
RE: st: xtivreg2 - Incomplete computation of first stage regression
Date
Sun, 28 Feb 2010 15:20:57 +0100
<>
See the discussion here:
http://www.stata.com/statalist/archive/2010-02/msg00974.html
The Stock Yogo tables only exist for up to three endogenous variables.
Eric
Eric de Souza
European Economic Studies
College of Europe
Brugge (Bruges)
Belgium
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Lim Boon Leong
Sent: 28 February 2010 09:45
To: [email protected]
Subject: <POSSIBLE SPAM>st: xtivreg2 - Incomplete computation of first stage regression
Dear Statalisters:
I have a regression equation which is consisted of 5 suspected endogenous variables with a list of excluded instrument variables. I use xtivreg2, fixed effect, cluster (id) small function to run the regression analysis.
Xtivreg2 lsalary ceodual ... (ownexr ownex2r ownex3r L.ldiv L.debt = lrisk lsale lsale2......), fe cluster(id) small
The regression runs smoothly and provides test results such as underidentification test, weak identification test, and Hansen J statistic.
But when I run the first function to get the first stage regression as
below:
Xtivreg2 lsalary ceodual ... (ownexr ownex2r ownex3r L.ldiv L.debt = lrisk lsale lsale2......), fe cluster(id) small first
, it only managed to provide first stage regression results for the first 3 endogenous variables namely ownexr, ownex2r, and ownex3r.
And for the fourth first stage regression result it is reported as such:
First-stage regression of _000007:
..........................
_000007 not found
r(111) which implies missing variables.
My questions are as follows:
1. If I have 5 endogenous regressors, can I expect to have 5 first
stage regression results for all these endogenous variables?
2. Is there any methods to compute the first stage regression for all
these 5 endogenous regressors?
Thank you very much.
Best regards,
Lim Boon Leong
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