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

Author

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  • Giorgio Calzolari

    (University of Florence)

  • Laura Magazzini

    (Department of Economics (University of Verona))

Abstract

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.

Suggested Citation

  • Giorgio Calzolari & Laura Magazzini, 2009. "Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood," Working Papers 53/2009, University of Verona, Department of Economics.
    • Handle: RePEc:ver:wpaper:53/2009
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    Cited by:

    1. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    2. Artūras Juodis, 2018. "Rank based cointegration testing for dynamic panels with fixed T," Empirical Economics, Springer, vol. 55(2), pages 349-389, September.
    3. Pardo Martínez, Clara Inés & Silveira, Semida, 2012. "Analysis of energy use and CO2 emission in service industries: Evidence from Sweden," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5285-5294.
    4. Pardo Martínez, Clara Inés, 2013. "An analysis of eco-efficiency in energy use and CO2 emissions in the Swedish service industries," Socio-Economic Planning Sciences, Elsevier, vol. 47(2), pages 120-130.

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