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Estimation of the Duration Model by Nonparametric Maximum Likelihood, Maximum Penalized Likelihood, and Probability Simulators

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  • Huh, Keun
  • Sickles, Robin C

Abstract

Failure to properly treat heterogeneity components in longitudinal analyses can result in an incorrect parametrization of the duration model. Estimation bias is not limited to duration dependence but also extends to the structural parameters. This paper uses Monte Carlo methods to examine the finite sample behavior of three estimators for this problem: nonparametric maximum likelihood, maximum penalized likelihood, and the probability simulator. The authors' results on the estimators' finite sample behavior for this class of model add to limited experimental evidence. They highlight the estimators' computational feasibility and point to their relative strengths in empirical duration modeling. Copyright 1994 by MIT Press.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics & Statistics.

Volume (Year): 76 (1994)
Issue (Month): 4 (November)
Pages: 683-94

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Handle: RePEc:tpr:restat:v:76:y:1994:i:4:p:683-94

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Cited by:
  1. Kalwij, Adriaan, 2001. "Individuals' Unemployment Durations over the Business Cycle," IZA Discussion Papers 369, Institute for the Study of Labor (IZA).
  2. Michael Baker & Angelo Melino, 1999. "Duration Dependence and Nonparametric Heterogeneity: A Monte Carlo Study," Working Papers melino-99-01, University of Toronto, Department of Economics.
  3. Raquel Carrasco & J. Ignacio García Pérez, 2012. "Economic Conditions and Employment Dynamics of Immigrants versus Natives: Who Pays the Costs of the “Great Recession”?," Working Papers 12.13, Universidad Pablo de Olavide, Department of Economics.
  4. Xiaohong Chen & James J. Heckman & Edward Vytlacil, 2000. "Identification and SQRT N Efficient Estimation of Semiparametric Panel Data Models with Binary Dependent Variables and a Latent Factor," Econometric Society World Congress 2000 Contributed Papers 1567, Econometric Society.
  5. Nicoletti, Cheti & Rondinelli, Concetta, 2010. "The (mis)specification of discrete duration models with unobserved heterogeneity: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 159(1), pages 1-13, November.
  6. Robin C. Sickles & Jenny Williams, 1999. "Turning from Crime: A Dynamic Perspective," School of Economics Working Papers 1999-08, University of Adelaide, School of Economics.
  7. Bloemen, H.G. & Kalwij, A.S., 1996. "Female Employment and Timing of Births Decisions: A Multiple State Transition Model," Discussion Paper 1996-26, Tilburg University, Center for Economic Research.
  8. Zhang, Tao, 2003. "A Monte Carlo study on non-parametric estimation of duration models with unobserved heterogeneity," Memorandum 25/2003, Oslo University, Department of Economics.
  9. Gaure, Simen & Røed, Knut & Zhang, Tao, 2005. "Time and Causality: A Monte Carlo Assessment of the Timing-of-Events Approach," Memorandum 19/2005, Oslo University, Department of Economics.
  10. George Neumann, 1996. "Search Models and Duration Data," Econometrics 9602008, EconWPA, revised 07 Mar 1996.
  11. Bloemen, Hans & Kalwij, Adriaan S., 2001. "Female labor market transitions and the timing of births: a simultaneous analysis of the effects of schooling," Labour Economics, Elsevier, vol. 8(5), pages 593-620, December.
  12. Kalwij, Adriaan, 2001. "Individuals' Unemployment Experiences: Heterogeneity and Business Cycle Effects," IZA Discussion Papers 370, Institute for the Study of Labor (IZA).

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