Julius,
You may also want to look at the bias-corrected LSDV estimator implemented in the Stata module xtlsdvc by Giovanni Bruno. See the following page:
http://ideas.repec.org/c/boc/bocode/s450101.html
Jean Salvati
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Julius Fr�d�ric Andr�
> Sent: Tuesday, November 29, 2005 3:19 AM
> To: [email protected]
> Subject: st: RE: Re: RE: panel data analysis using xtregar
>
> Thanks to Rodrigo and Steve for taking the time to comment, I
> will consider these in my work.
>
> Regards,
>
> JULIUS
>
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Rodrigo Alfaro
> Sent: 27 November 2005 17:17
> To: [email protected]
> Subject: st: Re: RE: panel data analysis using xtregar
>
> Julius, first lagged dependent variable (LDV) with xtreg, fe
> and xtregar is not the same. xtregar works like
> Cochrane-Orcutt regression (meaning you have the first
> observation... read Autocorrelation section in any textbook
> to learn about it)... for that purpose you have to set what
> kind of rho will be in your regression (option rhotype)...
> the default is Durbin-Watson.
> Details of the formula appear in the manual reference. If the
> coefficient of your LDV is near to 1 maybe you have a Unit
> Root case (note that this coefficient is downward-biased if
> it is positive Nickell 1968). There is a huge (and maybe more
> than that) about LDV+FE versus Arellano-Bond (1991, GMM
> estimator), Anderson-Hsiao (1981, IV estimator)... it seems
> to me that the bias generated by LDV+FE it is not so bad in
> compare with weak instruments.
> Hahn-Kuersteiner (2000) propose an asymptotic correction for
> that parameter... all these is available in Arellano's book. Rodrigo.
>
>
> ----- Original Message -----
> From: "Steve Stillman" <[email protected]>
> To: <[email protected]>
> Sent: Saturday, November 26, 2005 8:48 PM
> Subject: st: RE: panel data analysis using xtregar
>
>
> > Julius. It is not exactly clear what your model is here, but, in
> > general,
>
> > panel data models with lagged dependent variables cannot be
> > consistently estimated by directly entering the lagged
> variable as a
> > RHS variable. It sounds like you should have a look at xtabond and
> > the user-written
> > xtabond2 for your model.
> >
> > Cheers,
> > Steve
> >
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]]On Behalf Of Julius
> > Fr�d�ric Andr�
> > Sent: Sunday, November 27, 2005 7:26 AM
> > To: [email protected]
> > Subject: st: panel data analysis using xtregar
> >
> >
> > Dear statalist,
> >
> > Currently researching balanced panel data using insolvency
> prediction
> > estimates for different companies and regressing them (among other
> > controlling, time-variant variables) on time dummies to
> evaluate the
> > effects of a policy introduction in a certain year. I am
> using xtreg,
> > fe for evaluation, the panel is described by t=12 and
> i=110. I presume
> > autocorrelation of the insolvency predictor (ie. the dependent
> > variable) and therefore constructed a dataset with a lagged
> variable
> > for the insolvency predictor, hence also had to eliminate the first
> > period of the original dataset. It turned out that the estimator of
> > the lagged variable is indeed highly significant.
> >
> > Now I also consider using xtregar. Being new to stata (also
> to panel
> > data and time series analysis), however, I unfortunately could not
> > find the underlying formula used by Stata up to now. Would such an
> > autoregressive model be similar to the approach I did manually with
> > introducing a lagged variable and deleting the values of the first
> > period for each I? The stata result being different for the AR(1)
> > model and the fixed-effect model including a lagged variable do not
> > corroborate this assumption, so I assume some other
> underlying model.
> > I know that an AR appproach incorporates past values, but does this
> > mean the past error term, the past dependent or the past
> independent
> > variable (or all of them)? It would be of great help if
> someone could
> > provide a formula here or give a brief statement if I
> should resort to
> > xtregar at all if I assume autocorrelation anyways (so that the
> > applying AR is simply not necessary anymore).
> >
> > Additionally, I would like to backup my decision to include
> a lagged
> > variable in the model by testing on autocorrelation, using a
> > Durbin-Watson substitute for panel data, such as Bhargava et al. I
> > know that Stata can calculate this statistic when using xtregar,
> > however, it seems to me that the reported output is the
> result AFTER
> > applying the AR (1) model, indicating only the presence or
> absence (or
> > degree) of autocorrelation left after using the AR model? Is this
> > correct, or does the test statistic indicate autocorrelation before
> > using the AR model?
> >
> > Lastly, I am not sure whether I could also use the Baltagi-Wu test
> > score alternatively, seeing to it that I have a balanced
> dataset. Is
> > this test statistic only working with unbalanced data?
> >
> > The three preceding issues certainly are still on a somewhat basic
> > level, however, I would definitely appreciate useful
> comments on any
> > or all of these!
> >
> >
> > Thank you very much in advance,
> >
> > JULIUS ANDRE
> >
> >
> > *
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> >
> >
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> >
>
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