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Re: st: xtivreg2 Random Effects and Durbin Wu Hausman


From   Steven Archambault <archstevej@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtivreg2 Random Effects and Durbin Wu Hausman
Date   Wed, 26 Aug 2009 14:04:51 -0600

Hey Mark,

Maybe I am missing something. But, I don't see a statistic that is
equivalent to a Durbin-Wu-Hausman value when I call xtoverid. I have
added your suggested changes to the code.  I want to compare the
instrumentalized equation against the non-instrumentalized version.
Back to my original post, I am asking if a simple F-test would suffice
for this. Maybe I am not quite clear on something here.

Thanks,

Steve

On Wed, Aug 26, 2009 at 7:16 AM, Schaffer, Mark E<M.E.Schaffer@hw.ac.uk> wrote:
> Steve,
>
>> -----Original Message-----
>> From: Steven Archambault [mailto:archstevej@gmail.com]
>> Sent: 26 August 2009 00:07
>> To: statalist@hsphsun2.harvard.edu
>> Subject: xtivreg2 Random Effects and Durbin Wu Hausman
>>
>> Hi all,
>>
>> I have been playing around with testing for endogeneity in panel
>> regression models. Some of the methods discussed by Baum  (Intro. to
>> Econometrics Using Stata) to calculate DWH do not work with panel
>> data, and more so with Random Effects models. So, would it make sense
>> to use the following to calculate a DW F-statistic? What other methods
>> could be used?
>>
>>
>> xtreg policy instrument1 instrument2 gdp trade year, re robust;
>> predict policy_res, e;
>> xtreg invest policy trade gdp  year policy_res, re robust;
>> test policy_res;
>
> I'm pretty sure this doesn't work (or if it does, I haven't seen it
> demonstrated).
>
> Am I right to think that your RE estimation is this?
>
> xtivreg invest gdp trade year (policy=instrument1 instrument2), re
>
> and that you want robust or cluster-robust test of the exogeneity of
> policy?
>
> I think you can get -xtoverid- to do this for you, but it requires
> hacking the code and using the -noisily- option.
>
> You might want to make a copy of xtoverid.ado and call it
> myxtoverid.ado.  Then make the following changes:
>
> 1.  In the "program define" line at the top, change "program define
> xtoverid" to "program define myxtoverid".
>
> 2.  Look for the line that has
>
> if "`model'"=="g2sls" {
>
> Below that are three calls to -ivreg2-.
>
> 3.  At the end of the first two calls to -ivreg2-, after "noid", add the
> following (as a continuation of the same line):
>
> endog(`instd_g')
>
> 4.  At the end of the third call to -ivreg2-, after "noid" add
>
> endog(`inexog_g' `instd_g')
>
> 5.  After estimation, call -myxtoverid- with the -noisily- option.  The
> internal reestimation of the equation will be displayed and an
> endogeneity test for all endogenous regressors will be included.
>
> Note that if you estimate using EC2SLS, unless the panel is balanced the
> degrees of freedom of the test will be more than you expect and the test
> statistic will probably be misleadingly low.  The explanation has to do
> with (what seems to me, at any rate) the rather peculiar way exogenous
> regressors are treated by Stata's -xtivreg- in an unbalanced panel using
> EC2SLS.  There are more details in the last paragraph of the -xtoverid-
> help file.
>
> Cheers,
> Mark
>
>> Thanks,
>> Steve
>>
>
>
> --
> Heriot-Watt University is a Scottish charity
> registered under charity number SC000278.
>
>
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