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GEE estimation of the covariance structure of a bivariate panel data model with an application to wage dynamics and the incidence of profit-sharing in West Germany

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Abstract

We propose a generalized estimating equations (GEE) approach to the estimation of the mean and covariance structure of bivariate time series processes of panel data. The one-step approach allows for mixed continuous and discrete dependent variables. A Monte Carlo Study is presented to compare our particular GEE estimator with more standard GEE-estimators. In the empirical illustration, we apply our estimator to the analysis of individual wage dynamics and the incidence of profit-sharing in West Germany. Our findings show that time-invariant unobserved individual ability jointly influences individual wages and participation in profit sharing schemes.

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Authors and Affiliations

  1. DIW Berlin, University of Applied Sciences Bielefeld, Bielefeld, Germany

    Markus Pannenberg

  2. University of Hamburg, Hamburg, Germany

    Martin Spiess

Authors
  1. Markus Pannenberg
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  2. Martin Spiess
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Correspondence to Markus Pannenberg or Martin Spiess.

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We are grateful to an associate editor and two anonymous referees for their helpful comments and suggestions.

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Pannenberg, M., Spiess, M. GEE estimation of the covariance structure of a bivariate panel data model with an application to wage dynamics and the incidence of profit-sharing in West Germany. AStA Adv Stat Anal 93, 427–447 (2009). https://doi.org/10.1007/s10182-009-0117-2

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  • DOI: https://doi.org/10.1007/s10182-009-0117-2

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