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

  • Robert M. Sauer
  • Michael P. Keane

This paper develops a new simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can deal with the commonly encountered and widely discussed ``initial conditions problem,'' as well as the more general problem of missing state variables at any point during the sample period. Repeated sampling experiments on a dynamic panel data probit model with serially correlated errors indicate that the estimator has good small sample properties and is computationally practical for use with panels of the size that are likely to be encountered in practice

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Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 136.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:136
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  1. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
  2. Flinn, C.J., 1993. "Equilibrium Wage and Dismissal Processes," Working Papers 93-38, C.V. Starr Center for Applied Economics, New York University.
  3. 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.
  4. John F. Geweke & Michael P. Keane, 1997. "An empirical analysis of income dynamics among men in the PSID: 1968-1989," Staff Report 233, Federal Reserve Bank of Minneapolis.
  5. 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.
  6. Lee, L-F., 1990. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Papers 260, Minnesota - Center for Economic Research.
  7. 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.
  8. Arellano, M. & Honore, B., 2000. "Panel Data Models: Some Recent Developments," Papers 0016, Centro de Estudios Monetarios Y Financieros-.
  9. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
  10. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  11. 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.
  12. Card, David & Sullivan, Daniel G, 1988. "Measuring the Effect of Subsidized Training Programs on Movements in and out of Employment," Econometrica, Econometric Society, vol. 56(3), pages 497-530, May.
  13. 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.
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