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Robust priors in nonlinear panel data models

Author

Listed:
  • Manuel Arellano

    (Institute for Fiscal Studies and CEMFI)

  • Stéphane Bonhomme

    (Institute for Fiscal Studies and University of Chicago)

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.

Suggested Citation

  • Manuel Arellano & Stéphane Bonhomme, 2007. "Robust priors in nonlinear panel data models," CeMMAP working papers CWP07/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:07/07
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0707.pdf
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    References listed on IDEAS

    as
    1. Javier Álvarez & Manuel Arellano, 2004. "Robust Likelihood Estimation of Dynamic Panel Data Models," Working Papers wp2004_0421, CEMFI.
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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