Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79404
Authors: 
Arellano, Manuel
Bonhomme, Stéphane
Year of Publication: 
2006
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP07/07
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
Subjects: 
Panel data , incidental parameters , bias reduction , integrated likelihood , priors
JEL: 
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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