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st: RE: How can I correct ivreg2 coefficients for AR1?
> -----Original Message-----
> From: email@example.com
> [mailto:firstname.lastname@example.org] On Behalf Of adam dvir
> Sent: 13 September 2006 08:10
> To: email@example.com
> Subject: st: How can I correct ivreg2 coefficients for AR1?
> Dear all,
> My aim is to run 2sls regression with correction to
> auto-correlation AR(1).
> But I could not find a command that corrects AR(1) after
> "ivreg" command (the same way "prais" command does to a
> simple linear regression).
I think that's right - there isn't a built-in command.
> I heard that there is a user's command "ivreg2" that is able
> to do so, but I am not sure I know how it works. I tried to
> correct my regression with the
> bw(#) specification after ivreg2, but the coefficient did not
> change, only the standart errors.
This is because what you've done is ask ivreg2 for standard errors that
are robust to arbitrary autocorrelation. It's just like the usual
-robust- option - the standard errors become consistent, but the
coefficients don't change because they were already consistent (by
assumption), albeit possibly inefficient.
Note that this is a correction for arbitrary autocorrelation, so it's
more general than just your AR(1) case.
The bw(#) option specifies how quickly the autocorrelation is assumed to
die out. The literature on this is pretty technical; what number you
choose depends among other things on the kernel estimator used and the
sample size. One rule of thumb for the Barlett (a.k.a. Newey-West)
kernel is bandwidth = 0.75 * T^1/3 where T is the sample size. See
Stock-Watson, Introduction to Econometrics, pp. 505-6.
> I tried the "gmm" specification, and this time the
> coefficients did change, but I am not sure if the results are
> IV coefficients or GMM's.
They are GMM coefficients. The difference is that they are efficient in
the presence of arbitrary autocorrelation (not just consistent like the
> Can you please direct me to the right answer?
> thank you very much
> Adam Dvir
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