First Maybe you should beter find out the lag structure of your variables by
var lag selection.
Second Arelano-Bond test for serial correlation for first order is not
significant. It is also expected to have a first order autocorrelation in a
negative form. This is stated in Baltagi (2001) and Arellano (2003)boks.
So checking for the second order autocorrelation will be enough. Do not
skip Sargan test on the final.
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Viktor Slavtchev
Sent: Tuesday, December 18, 2007 3:12 PM
To: [email protected]
Subject: st: xtabond2
Dear Statalisters,
I also have some questions regarding the proper implementation of
-xtabond2-.
The guidance provided by David Roodman is doubtless wonderful peace of
work - it is quite straightforward and makes a lot of things clear.
However, there are several questions I could not answer by myself. Thats
why I am asking for your help.
I am trying to estimate dynamic panel model like that:
y[i,t] = y[i,t-1] + w[i,t] + v[i] + e[i,t]
where in the notation of the xtabond2-help y = dependent, w =
covariate(s) (apropos, there are some theoretical arguments to suspect
'w' endogenous), v = fixed effect, e = residuals.
I am trying to estimate the model in differences (not levels).
xtabond2 ln_y l1.ln_y ln_w, robust noleveleq gmm(l1.ln_y) gmm(ln_w).
I am not very sure that I understood the xtabond2-syntaxes correctly, so
the first question is whether this is the right way to estimated the model.
Second, Arelano-Bond test for serial correlation suggests first order
autocorrelation, rejects however second order AC.
Hence, I am asking myself whether the first lags are proper instruments
or not and whether I should start with deeper lags. For example:
xtabond2 ln_y l1.ln_y ln_w, robust noleveleq gmm(l1.ln_y) gmm(ln_w,
lag(2 .)).
Thanks for any help.
Viktor
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