Robust priors in nonlinear panel data models

Manuel Arellano and Stéphane Bonhomme

No CWP07/07, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2007-03-21
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Robust Priors in Nonlinear Panel Data Models (2009)
Working Paper: Robust Priors in Nonlinear Panel Data Models (2006)
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