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

  • Michael P. Keane

    ()

    (University of Technology Sydney and Arizona State University)

  • Robert M. Sauer

    (University of Bristol)

This 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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File URL: http://www.macro.economics.uni-mainz.de/RePEc/pdf/Discussion_Paper_1008.pdf
File Function: First version, 2010
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Paper provided by Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz in its series Working Papers with number 1008.

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Length: 70 pages
Date of creation: 05 Jul 2010
Date of revision: 05 Jul 2010
Handle: RePEc:jgu:wpaper:1008
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  1. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
  2. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics, Springer, vol. 7(4), pages 343-376, December.
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  6. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(04), pages 518-552, December.
  7. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296 Elsevier.
  8. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  9. Keane, Michael P & Wolpin, Kenneth I, 2001. "The Effect of Parental Transfers and Borrowing Constraints on Educational Attainment," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(4), pages 1051-1103, November.
  10. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568 Elsevier.
  11. Poterba, James M & Summers, Lawrence H, 1995. "Unemployment Benefits and Labor Market Transitions: A Multinomial Logit Model with Errors in Classification," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 207-16, May.
  12. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
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