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Semiparametric estimation of a panel data proportional hazards model with fixed effects

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  • Horowitz, Joel L.
  • Lee, Sokbae

Abstract

This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finitedimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also presents a method for estimating the parametric part of the proportional hazards model with dependent right censoring, under which the partial likelihood estimator is inconsistent. The paper presents some Monte Carlo evidence on the small sample performance of the new estimators.
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Suggested Citation

  • Horowitz, Joel L. & Lee, Sokbae, 2004. "Semiparametric estimation of a panel data proportional hazards model with fixed effects," Journal of Econometrics, Elsevier, vol. 119(1), pages 155-198, March.
    • Handle: RePEc:eee:econom:v:119:y:2004:i:1:p:155-198
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    1. Jinyong Hahn, 1994. "The Efficiency Bound of the Mixed Proportional Hazard Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 607-629.
    2. Gorgens, Tue & Horowitz, Joel L., 1999. "Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable," Journal of Econometrics, Elsevier, vol. 90(2), pages 155-191, June.
    3. Efromovich S., 2001. "Density Estimation Under Random Censorship and Order Restrictions: From Asymptotic to Small Samples," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 667-684, June.
    4. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    5. Bo E. Honoré, 1993. "Identification Results for Duration Models with Multiple Spells," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(1), pages 241-246.
    6. Bowlus, Audra J & Kiefer, Nicholas M & Neumann, George R, 2001. "Equilibrium Search Models and the Transition from School to Work," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(2), pages 317-343, May.
    7. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    8. Farber, Henry S, 1994. "The Analysis of Interfirm Worker Mobility," Journal of Labor Economics, University of Chicago Press, vol. 12(4), pages 554-593, October.
    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. Ridder, Geert & Tunali, Insan, 1999. "Stratified partial likelihood estimation," Journal of Econometrics, Elsevier, vol. 92(2), pages 193-232, October.
    11. Robert H. Topel & Michael P. Ward, 1992. "Job Mobility and the Careers of Young Men," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(2), pages 439-479.
    12. Jaap H. Abbring & Pierre-André Chiappori & Jean Pinquet, 2003. "Moral Hazard and Dynamic Insurance Data," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 767-820, June.
    13. Horowitz, Joel L, 2001. "Nonparametric Estimation of a Generalized Additive Model with an Unknown Link Function," Econometrica, Econometric Society, vol. 69(2), pages 499-513, March.
    14. Jaap H. Abbring & Pierre-André Chiappori & Jean Pinquet, 2003. "Moral Hazard and Dynamic Insurance Data," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 767-820, June.
    15. 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 103-137, January.
    16. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
    17. Joel L. Horowitz, 1999. "Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 67(5), pages 1001-1028, September.
    18. Jovanovic, Boyan, 1979. "Job Matching and the Theory of Turnover," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 972-990, October.
    19. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    20. Horowitz, Joel & Hardle, Wolfgang, 1994. "Direct Semiparametric Estimation of Single-Index Models With Discrete Covariates," Working Papers 94-22, University of Iowa, Department of Economics.
    21. Tiemen Woutersen, 2000. "Estimators for Panel Duration Data with Endogenous Censoring and Endogenous Regressors," Econometric Society World Congress 2000 Contributed Papers 1581, Econometric Society.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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