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A Computationally Practical Simulation Estimation Algorithm For Dynamic Panel Data Models With Unobserved Endogenous State Variables

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  • Michael P. Keane
  • Robert M. Sauer

Abstract

This article develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate the estimator has good small sample properties. We apply the estimator to a model of female labor supply and show that the rarely used Polya model fits the data substantially better than the popular Markov model. The Polya model also produces far less state dependence and many fewer race effects and much stronger effects of education, young children, and husband's income on female labor supply decisions.

Suggested Citation

  • Michael P. Keane & Robert M. Sauer, 2010. "A Computationally Practical Simulation Estimation Algorithm For Dynamic Panel Data Models With Unobserved Endogenous State Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(4), pages 925-958, November.
  • Handle: RePEc:ier:iecrev:v:51:y:2010:i:4:p:925-958
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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