sistoand80 <[email protected]>:
> Dear Dr. Bruno,
> I'm Andrea Sisto, a PHD student in Economics at the Univeristy of Turin. As
> I'm working with Prof. Zanola, I thank you also on behalf of Roberto. Our
> problem is that we have to account for heterogeneity in panel. This
> heterogeneity should be captured introducing cross-section dummy variable.
> But standard GMM DP estimators drop individual effect with first-difference.
> For ivreg2, the question was whether a FE2SLS, with a model in level with
> Cross section dummy variables, was a correct way to deal with dynamic panel
> (our strategy was to simply instrument the dependent variable with some
> exogenous instruments).
> Thank you for your suggestion
> AS
Careful here. Time invariant individual heterogeneity is
typically accommodated either 1) by adding individual dummies,
which boils down to transforming variables in deviations from the
group means; or 2) by first-differencing. So at the end of the day,
*both* methods actually do the same job of purging the regression
from unobserved individual heterogeneity.
First-differencing is held as a more convenient transformation
in dynamic panel data models since under conventional assumptions
on the var-cov matrix of disturbances in levels first-differenced
disturbances are not correlated to past realizations of the
dependent variable, which can be used as instruments. A reason not
to do that is a non-spherical var-cov matrix of disturbances in
levels, e.g. due to within-group serial correlation. In this case,
lagged values of y may not be used as instruments, but lags (and
leads) of strictly exogenous explanatory variables can always be
exploited to identify the relationship of interest (in levels or
first-differences) and -ivreg2- can certainly take care of all
estimation issues. Nevertheless, such IV estimators might have poor
finite sample performances.
Useful readings are the first part of the Arellano-Honore (2001)
chapter or the Arellano (2003) manual.
Arellano, M. 2003. Panel Data Econometrics. Oxford: Oxford
University Press
Arellano, M. and B. Honor�, 2001. Panel Data Models:
Some Recent Developments. In Handbook of Econometrics vol. 5
ed. J.J. Heckman and E. Leamer. Amsterdam: Elsevier
Giovanni
>
>
>
>
>
> > Roberto Zanola <[email protected]>:
> >
> > > Dear all,
> > > we need to estimate a dynamic short panel (T=6 and N=20). Two
> possibilities:
> > > (1) lsdvc
> >
> > This is implemented in Stata by the user written code -xtlsdvc-.
> > It behaves relatively better than IV-GMM estimators in small panel
> > data-sets, in terms of both bias and root mean squared error,
> > but needs strictly exogeneity of regressors and neither
> > heteroskedasticity or serial correlation of disturbances.
> >
> > > (2) ivreg2 with dummies
> >
> > I'm not clear what estimator Roberto has in mind in this
> > case. -ivreg2- is a flexible routine that can implement many
> > IV estimators and tests, and clearly not all of them are appropriate
> > methods for dynamic models. A simple N-consistent estimator for
> > dynamic panel data models that can be supported by -ivreg2- is that
> > developed by Anderson and Hsiao (1982). It is carried out by taking
> > variables in first-differences and using the dependent variable
> > lagged two times, y(t-2), as an instrument for Dy(t-1). One can
> > also deal with endogenous x's in this case, provided valid instruments
> > are available. However, Monte Carlo evidence demonstrates that the AH
> > estimator, although virtually unbiased, is rather imprecise in
> > small samples (very large root mean squared error).
> >
> > References
> > Anderson, T. W. and C. Hsiao. 1982. Formulation and Estimation
> > of Dynamic Models Using Panel Data. Journal of Econometrics
> > 18: 570�606
> >
> > Roberto may also find it useful my paper on small panel data-sets,
> > downloadable from
> >
> > http://ideas.repec.org/p/cri/cespri/wp165.html
> >
> >
> > Giovanni
> > (author of -xtlsdvc-)
> >
> > >
> > > *
> > > * For searches and help try:
> > > * http://www.stata.com/support/faqs/res/findit.html
> > > * http://www.stata.com/support/statalist/faq
> > > * http://www.ats.ucla.edu/stat/stata/
> > >
> >
> >
> > --
> > Giovanni S.F. Bruno
> > http://ideas.repec.org/e/pbr136.html
> > Istituto di Economia Politica, Universit� Bocconi
> > Via U. Gobbi, 5, 20136 Milano
> > Italy
> > tel. + 02 5836 5411
> > fax. + 02 5836 5438
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
>
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
Giovanni S.F. Bruno
http://ideas.repec.org/e/pbr136.html
Istituto di Economia Politica, Universit� Bocconi
Via U. Gobbi, 5, 20136 Milano
Italy
tel. + 02 5836 5411
fax. + 02 5836 5438
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
