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Multilevel and nonlinear panel data models

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This paper presents a selective survey on panel data methods. The focus is on new developments. In particular, linear multilevel models, specific nonlinear, nonparametric and semiparametric models are at the center of the survey. In contrast to linear models there do not exist unified methods for nonlinear approaches. In this case conditional maximum likelihood methods dominate for fixed effects models. Under random effects assumptions it is sometimes possible to employ conventional maximum likelihood methods using Gaussian quadrature to reduce a T-dimensional integral. Alternatives are generalized methods of moments and simulated estimators. If the nonlinear function is not exactly known, nonparametric or semiparametric methods should be preferred.

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Authors and Affiliations

  1. Institute of Quantitative Economic Research, University of Hannover, Königsworther Platz 1, 30167, Hannover, Germany

    Olaf Hübler

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  1. Olaf Hübler
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Helpful comments and suggestions from an unknown referee are gratefully acknowledged.

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Hübler, O. Multilevel and nonlinear panel data models. Allgemeines Statistisches Arch 90, 121–136 (2006). https://doi.org/10.1007/s10182-006-0225-1

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  • DOI: https://doi.org/10.1007/s10182-006-0225-1

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