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st: RE: RE: autocorrelation and endogeneity


From   "Yu-Luen Ma" <yma@ilstu.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: autocorrelation and endogeneity
Date   Thu, 22 May 2008 17:00:46 -0500

Mark,
 
Thanks for your response. I do have panel data but I use one-way fixed effects (only control for time) and thus ivreg2 works. It sounds like the -cluster- will work for me. 
 
Yuluen
 
________________________________

From: owner-statalist@hsphsun2.harvard.edu on behalf of Schaffer, Mark E
Sent: Thu 5/22/2008 4:49 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: RE: autocorrelation and endogeneity



Yuluen,

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Yu-Luen Ma
> Sent: 22 May 2008 22:26
> To: statalist@hsphsun2.harvard.edu
> Subject: st: autocorrelation and endogeneity
>
> I would like to run a model that correct for both endogeneity
> and autocorrelation. Does the command ivreg2 with gmm2s
> robust cluster(id) supports the estimation of
> autocorrelation-robust covariance matrix and standard errors?
> I assume that the combination of robust and cluster(id) does
> that. If not, could anyone suggest a command that would
> correct for autocorrelation? Thanks.

-cluster- allow consistent estimation the presence of a very specific
kind of autocorrelation.  If you have panel data, and the observations
within each panel are autocorrelated but independent across panels,
-cluster- will give you consistent estimates.

It sounds like this isn't what you want.  If your application is
straight time series, you can use -ivreg2- and a kernel-type var-cov
estimator to get autocorrelation-consistent estimates.  Have a look at
the -bw- and -kernel- options.

Cheers,
Mark

Prof. Mark Schaffer
Department of Economics
School of Management & Languages
Heriot-Watt University
Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3296
email: m.e.schaffer@hw.ac.uk
web: http://www.sml.hw.ac.uk/ecomes


> 
> Yuluen
>
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