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RE: st: model selection using information criteria with xtlsdvc or xtabond2
From
Cameron McIntosh <[email protected]>
To
STATA LIST <[email protected]>
Subject
RE: st: model selection using information criteria with xtlsdvc or xtabond2
Date
Tue, 22 Nov 2011 21:59:02 -0500
Sebastian,
Perhaps you can use the natural log of the residual sum of squares, e.g.,
AIC = n*ln(SSR) + 2*k
BIC = n*ln(SSR) + k*ln(n)
where n is the sample size and k is the number of estimated parameters.
Yamaoka, K., Nakagawa, T., & Uno, T. (1978). Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. Journal of Pharmacokinetics and Biopharmaceutics, 6, 165–175.
Bonate, P.L. (2011). Pharmacokinetic-Pharmacodynamic Modeling and Simulation (2nd ed.). New York, NY: Springer.
My two cents,
Cam
----------------------------------------
> From: [email protected]
> Date: Tue, 22 Nov 2011 17:34:24 -0800
> Subject: st: model selection using information criteria with xtlsdvc or xtabond2
> To: [email protected]
>
> Dear statalisters,
>
> I am running a highly unbalanced large-T, small-N (T: ~30, N: ~50-150)
> dynamic panel estimation using Bruno's xtlsdvc estimator. Naturally,
> the estimator doesn't provide information on the log-likelihood (as
> does xtabond/xtabond2). I still would like to do model selection using
> the standard information criteria, like Akaike's AIC or the BIC. Is
> there a well-functioning work-around avoiding the use of the
> log-likelihood?
>
> Thanks
>
> Sebastian
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