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Robust priors in nonlinear panel data models

Authors: Manuel Arellano and Stéphane Bonhomme
Date: 01 March 2009
Type: Journal Article, Econometrica, Vol. 77, No. 2, pp. 489-536
DOI: 10.3982/ECTA6895

Abstract

Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall in this category. We provide a characterization of the class of weights (or priors) that produce estimators that are first-order unbiased. We show that such bias-reducing weights must depend on the data unless an orthogonal reparameterization or an essentially equivalent condition is available. Two intuitively appealing weighting schemes are discussed. We argue that asymptotically valid confidence intervals can be read from the posterior distribution of the common parameters when N and T grow at the same rate. Finally, we show that random effects estimators are not bias reducing in general and discuss important exceptions. Three examples and some Monte Carlo experiments illustrate the results.

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Previous version:
Manuel Arellano and Stéphane Bonhomme March 2007, Robust priors in nonlinear panel data models, cemmap Working Paper, CWP07/07

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