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Estimating panel data duration models with censored data

  • Sokbae 'Simon' Lee

    ()

    (Institute for Fiscal Studies and University College London)

This paper presents a method for estimating a class of panel data duration models, under which an unknown transformation of the duration variable is linearly related to the observed explanatory variables and the unobserved heterogeneity (or frailty) with completely known error distributions. This class of duration models includes a panel data proportional hazards model with fixed effects. The proposed estimator is shown to be n1/2-consistent and asymptotically normal with dependent right censoring. The paper provides some discussions on extending the estimator to the cases of longer panels, multiple states, and endogenous explanatory variables. Some Monte Carlo studies are carried out to illustrate the finite-sample performance of the new estimator.

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File URL: http://cemmap.ifs.org.uk/wps/cwp0313.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP13/03.

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Length: 17 pp.
Date of creation: Sep 2003
Date of revision:
Handle: RePEc:ifs:cemmap:13/03
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  1. Newman, John L & McCulloch, Charles E, 1984. "A Hazard Rate Approach to the Timing of Births," Econometrica, Econometric Society, vol. 52(4), pages 939-61, July.
  2. Honore, Bo E, 1990. "Simple Estimation of a Duration Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 58(2), pages 453-73, March.
  3. Jaap H. Abbring & Pierre-Andre Chiappori, 2004. "Moral Hazard and Dynamic Insurance Data," 2004 Meeting Papers 316, Society for Economic Dynamics.
  4. Tue Gorgens & Joel L. Horowitz, 1996. "Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable," Econometrics 9603001, EconWPA.
  5. Horowitz, Joel L, 1996. "Semiparametric Estimation of a Regression Model with an Unknown Transformation of the Dependent Variable," Econometrica, Econometric Society, vol. 64(1), pages 103-37, January.
  6. Ridder, G. & Tunali, I., 1997. "Stratified Partial Likelihood Estimation," Papers 1997/17, Koc University.
  7. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-30, November.
  8. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  9. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
  10. Robert H. Topel & Michael P. Ward, 1988. "Job Mobility and the Careers of Young Men," NBER Working Papers 2649, National Bureau of Economic Research, Inc.
  11. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
  12. Honore, Bo E, 1993. "Identification Results for Duration Models with Multiple Spells," Review of Economic Studies, Wiley Blackwell, vol. 60(1), pages 241-46, January.
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