A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables
AbstractThis paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed "initial conditions problem," as well as the more general problem of missing state variables during the sample period. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate that the estimator has good small sample properties. We apply the estimator to a model of married women's labor force participation decisions. The results show that the rarely used Polya model, which is very difficult to estimate given missing data problems, fits the data substantially better than the popular Markov model. The Polya model implies far less state dependence in employment status than the Markov model. It also implies that observed heterogeneity in education, young children and husband income are much more important determinants of participation, while race is much less important.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4054.
Length: 70 pages
Date of creation: Mar 2009
Date of revision:
Publication status: published in: International Economic Review, 2010, 51(4), 925-958
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Other versions of this item:
- 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.
- Sauer, Robert & Keane, Michael P., 2007. "A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables," Discussion Paper Series In Economics And Econometrics 0705, Economics Division, School of Social Sciences, University of Southampton.
- Robert M. Sauer & Michael P. Keane, 2004. "A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables," Econometric Society 2004 North American Summer Meetings 136, Econometric Society.
- Michael P. Keane & Robert M. Sauer, 2010. "A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables," Working Papers 1008, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 05 Jul 2010.
- 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
- J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
- J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-03-22 (All new papers)
- NEP-CMP-2009-03-22 (Computational Economics)
- NEP-ECM-2009-03-22 (Econometrics)
- NEP-LAB-2009-03-22 (Labour Economics)
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