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

  • Sauer, Robert
  • Keane, Michael P.

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. 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.
  2. 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.
  3. Paul A. Ruud., 1988. "Extensions of Estimation Methods Using the EM Algorithm.," Economics Working Papers 8899, University of California at Berkeley.
  4. Flinn, Christopher J, 1997. "Equilibrium Wage and Dismissal Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 221-36, April.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  10. 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.
  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. 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.
  13. 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.
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