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Modern Econometric Analysis pp 119–135Cite as

Multilevel and Nonlinear Panel Data Models

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Abstract

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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These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

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Author information

Authors and Affiliations

  1. Institute of Empirical Economic Research, University of Hannover, Hannover

    Olaf Hübler

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  1. Olaf Hübler
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Editors and Affiliations

  1. Empirical Economic Research and Econometrics, University of Hannover, Königsworther Platz 1, 30167, Hannover, Germany

    Olaf Hübler

  2. University of Bielefeld, Universitätsstraße 25, 33615, Bielefeld, Germany

    Jachim Frohn

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Hübler, O. (2006). Multilevel and Nonlinear Panel Data Models. In: Hübler, O., Frohn, J. (eds) Modern Econometric Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32693-6_9

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