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- W3122902492 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." @default.
- W3122902492 created "2021-02-01" @default.
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- W3122902492 date "2007-03-21" @default.
- W3122902492 modified "2023-09-25" @default.
- W3122902492 title "Robust priors in nonlinear panel data models" @default.
- W3122902492 doi "https://doi.org/10.1920/wp.cem.2007.0707" @default.
- W3122902492 hasPublicationYear "2007" @default.
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