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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.

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File URL: http://hdl.handle.net/10.1111/j.1468-2354.2010.00606.x
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Bibliographic Info

Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 51 (2010)
Issue (Month): 4 (November)
Pages: 925-958

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Handle: RePEc:ier:iecrev:v:51:y:2010:i:4:p:925-958

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  1. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(4), pages 1051-1103, November.
  2. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, Elsevier, vol. 49(3), pages 305-341, September.
  3. Daniel A. Ackerberg, 2001. "A New Use of Importance Sampling to Reduce Computational Burden in Simulation Estimation," NBER Technical Working Papers 0273, National Bureau of Economic Research, Inc.
  4. J. Geweke & M. Keane, . "An Empirical Analysis of Income Dynamics among Men in the PSID: 1968–1989," Institute for Research on Poverty Discussion Papers 1127-97, University of Wisconsin Institute for Research on Poverty.
  5. 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.
  6. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, Econometric Society, vol. 57(5), pages 1027-57, September.
  7. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568 Elsevier.
  8. David Card & Daniel Sullivan, 1987. "Measuring the Effect of Subsidized Training Programs on Movements In andOut of Employment," NBER Working Papers 2173, National Bureau of Economic Research, Inc.
  9. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, Elsevier, vol. 87(2), pages 239-269, September.
  10. Flinn, Christopher J, 1997. "Equilibrium Wage and Dismissal Processes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 15(2), pages 221-36, April.
  11. Arellano, M. & Honore, B., 2000. "Panel Data Models: Some Recent Developments," Papers, Centro de Estudios Monetarios Y Financieros- 0016, Centro de Estudios Monetarios Y Financieros-.
  12. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, INFORMS, vol. 15(1), pages 1-20.
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