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A computationally practical simulation estimation algorithm for dynamic panel data models with unobserved endogenous state variables

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  • Sauer, Robert
  • Keane, Michael P.

Abstract

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. Keywords; initial conditions, missing data, discrete choice, simulation estimation

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File URL: http://eprints.soton.ac.uk/35074/1/Econ_discussion_0705.pdf
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Paper provided by Economics Division, School of Social Sciences, University of Southampton in its series Discussion Paper Series In Economics And Econometrics with number 0705.

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Date of creation: 01 Aug 2007
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Handle: RePEc:stn:sotoec:0705

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  1. Paul A. Ruud., 1988. "Extensions of Estimation Methods Using the EM Algorithm.," Economics Working Papers 8899, University of California at Berkeley.
  2. 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.
  3. 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.
  4. 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.
  5. Flinn, Christopher J, 1997. "Equilibrium Wage and Dismissal Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 221-36, April.
  6. 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.
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  8. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  9. 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.
  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. 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.
  12. Arellano, M. & Honore, B., 2000. "Panel Data Models: Some Recent Developments," Papers 0016, Centro de Estudios Monetarios Y Financieros-.
  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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