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Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood

  • Giorgio Calzolari

    ()

    (University of Florence)

  • Laura Magazzini

    ()

    (Department of Economics (University of Verona))

In a panel data model with random effects, when autocorrelation in the error is considered, (Gaussian) maximum likelihood estimation produces a dramatically large number of corner solutions: the variance of the random effect appears (incorrectly) to be zero, and a larger autocorrelation is (incorrectly) assigned to the idiosyncratic component. Thus heterogeneity could (incorrectly) be lost in applications to panel data with customarily available time dimension, even in a correctly specified model. The problem occurs in linear as well as nonlinear models. This paper aims at pointing out how serious this problem can be (largely neglected by the panel data literature). A set of Monte Carlo experiments is conducted to highlight its relevance, and we explain this unpleasant effect showing that, along a direction, the expected log-likelihood is nearly flat. We also provide two examples of applications with corner solutions.

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Paper provided by University of Verona, Department of Economics in its series Working Papers with number 53/2009.

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Date of creation: Feb 2009
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Handle: RePEc:ver:wpaper:53/2009
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  1. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
  2. Sune Karlsson & Jimmy Skoglund, 2000. "Maximum-Likelihood Based Inference in the Two-Way Random Effects Model with Serially Correlated Time Effects," Econometric Society World Congress 2000 Contributed Papers 1178, Econometric Society.
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