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Re: st: Goodness of fit of xtpcse


From   Gordon Hughes <G.A.Hughes@ed.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Goodness of fit of xtpcse
Date   Wed, 25 Jan 2012 11:18:49 +0000

A somewhat belated answer to this question. The coefficients generated by -xtpcse- are identical to those generated by pooled OLS if autocorrelation is suppressed and identical to those generated by the pooled Prais-Winsten estimator if autocorrelation is permitted. In each case the routine generates a corrected set of standard errors. It follows that you can use either -regress- or -prais- to generate any measures of goodness of fit which depend upon the coefficients rather than their standard errors.

But ... you shouldn't rely upon log-likelihoods or associated measures for this purpose, because the statistical assumptions for -xtpcse- do not correspond to the assumptions used to construct the likelihood function for OLS or Prais-Winsten models..

A further qualification: the R^2 value reported by -xtpcse- can be greater than 1, though only, I think, when autocorrelation is included. Hence, you should be careful about relying upon that as well.

Gordon Hughes
g.a.hughes@ed.ac.uk

==============
Dear in Stata-list

I am looking for a goodness of fit measure for pooled time
series models using xtpcse other than R^2.

Unfortunately, Stata does not provide log-likelihood values (for calculating BIC or AIC), am I right?


Is there a way to get log-likelihood valus when using xtpcse?


Regards

Talal

University of Southampton


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