RE: st: model selection using information criteria with xtlsdvc or xtabond2

[Cameron McIntosh <cnm100@hotmail.com>]

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: sebastian.petrick@ifw-kiel.de
> Date: Tue, 22 Nov 2011 17:34:24 -0800
> Subject: st: model selection using information criteria with xtlsdvc or xtabond2
> To: statalist@hsphsun2.harvard.edu
>
> 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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