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Re: st: Testing the validity of instruments when estimating a GMM model with

From   Natalie Trapp <[email protected]>
To   [email protected]
Subject   Re: st: Testing the validity of instruments when estimating a GMM model with
Date   Wed, 14 Mar 2012 18:02:02 +0100

Am 12/03/2012 21:54, schrieb Christopher Baum:
I estimate a GMM model with Windmeijer corrected robust standard errors
as follows

xtdpd y l3.y_rain x1 x2 x3 x4 x5, dgmmiv(l3.y_rain, lag(2 3))  dgmmiv(x1
x2 x3, lag(2) ) lgmmiv(x1 x2 x3,lag(2)) div(x4 x5 ) twostep hascons

When using the vce(vcetype) option, the sargan statistic cannot be
obtained because the distribution of the Sargan test is unknown when the
disturbances are heteroscedastic. However, I need to test the validity
of over-identifying restrictions and was thinking whether one can use
the same model without a Windmeijer correction (without vce(robust)) to
test for the validity of over-identifying restrictions and then infer on
the robust model?

The Sargan test assumes iid errors. The xtdpd command refuses to compute same with a robust VCE.
But there is always a Hansen J statistic available, and it is robust to non-iid errors. I suggest you use
David Roodman's xtabond2 (available from SSC) for these models; it will produce both a Sargan
and a Hansen J test, and warn you about their various shortcomings. It will also allow you to estimate all
DPD models with a single command, rather than Stata's three official commands, and provides several
features missing from Stata's commands.


Kit Baum   |   Boston College Economics&  DIW Berlin   |
                              An Introduction to Stata Programming  |
   An Introduction to Modern Econometrics Using Stata  |

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Dear Kit,

thank you very much for your quick response.

I would like to use xtdpd because it also allows for predetermined variables. However, if there is no test for xtdpd, I should use xtabond2 and the Hansen J.

Kind regards,

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