Re: st: 2SLS with random effects correcting for autocorrelation

[Helene Ehrhart <Helene.Ehrhart@u-clermont1.fr>]

Thank you Nicola for these helpful suggestions.
I'll try them!

Hélène.

Quoting nicola.baldini2@unibo.it:

> No suggestions that 100% fit your problem.
> Among second best solutions, you may leave the random effects. In   
> such a case, an uncommon choice is to try the -fmivreg- from   
> http://www.antonisureda.com/other/stuff/files/ which uses the Fama   
> and MacBeth (1973) two step procedure, with instrumental variables   
> and Newey-West standard errors. In the first step, for each single   
> time period a cross-sectional regression is performed. Then, in the   
> second step, the final coefficient estimates are obtained as the   
> average of the first step coefficient estimates.
> Fama, Eugene F., and James D. MacBeth, 1973. Risk, Return, and   
> Equilibrium: Empirical tests, Journal of Political Economy 81,   
> 607-636.
> Other options (not involving the fixed effects) are first   
> differencing (-xitvreg2- and -xtabond2-) or cross-sectional commands  
>  (-ivreg2-, -newey2-), all of them are available from SSC.
> Nicola
>
> P.S. I'll NOT receive/read any email but the Digest.
>
> At 02.33 21/03/2009 -0400, Helene Ehrhart wrote:
> > Dear all,
> >
> > I would like to estimate an equation with instrumental variables using
> > random effects and correcting for autocorrelation.
> > I already tried many ways to do that in stata but no command was able
> > to meet all the three requirements :
> >
> > - - xtivreg , re does not allow for autocorrelation correction
> > - - xtivreg2 does not allow for random effects
> > - - xtdata to transform data so that it corresponds to random effects
> > and then estimation with ivreg28 but then the option bw(1) is not
> > possible since the data are transformed
> > - - estimating the first stage with xtreg ,re ; taking the predicted
> > dependant value and using it as regressors for the 2nd stage using
> > xtregar ,re which corrects for autocorrelation. This works but the
> > standard errors should then be corrected by bootstrapping to correct
> > the bias of using a predicted value as regressor. Unfortunately,
> > traditional bootstrap using a random sample does not maintain the
> > autocorrelation structure. So this method should also be eliminated.
> >
> > If anyone already faced this problem, estimating in 2SLS with random
> > effects correcting for autocorrelation, I would really appreciate to
> > know what is the proper way to do that in Stata.
> >
> > Thanks,
> >
> > Hélène Ehrhart.




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