A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables

Michael P. Keane, Robert M. Sauer

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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.
Original languageEnglish
Pages (from-to)925-958
Number of pages34
JournalInternational Economic Review
Issue number4
Early online date18 Nov 2010
Publication statusPublished - Nov 2010


  • Initial Conditions
  • Missing Data
  • Classification Error
  • Simulated Maximum Likelihood
  • Female Labor Supply

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