A COMPUTATIONALLY PRACTICAL SIMULATION ESTIMATION ALGORITHM FOR DYNAMIC PANEL DATA MODELS WITH UNOBSERVED ENDOGENOUS STATE VARIABLES* |
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| Authors: | Michael P Keane Robert M Sauer |
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| Institution: | 1. University of Technology Sydney, Australia, and Arizona State University;2. University of Bristol;3. This research is partially supported by the Australian Research Council, through a grant to Michael Keane (ARC grant numbers FF0561843 and DP0774247), and the Economic and Social Research Council of the United Kingdom, through a grant to Robert Sauer (ESRC grant number RES‐000‐22–1529). Please address correspondence to: Robert M. Sauer, Department of Economics, University of Bristol, 8 Woodland Road, Bristol BS8 1TN, U.K. Phone: +44(0)117 928 8421. E‐mail: . |
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| 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. |
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