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Semiparametric Estimation of Single-Index Transition Intensities

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  • Gorgens, T.

Abstract

This research develops semiparametric kernel-based estimators of state-specific conditional transition intensitiesm, hs (y|x), for duration models with right-censoring and/or multiple destinations (competing risks). Both discrete and continous duration data are considered. The maintained assumptions are that hs(y|x) depends on x only through an index x'Bs. In contrast to existing semiparametric estimators, proportional intensities is not assumed. The new estimators are asymptotically normally distributed. The estimator of Bs is root-n consistent. The estimator of hs (y|x) achieves the one-dimensional rate of convergence. Thus the single-index assumption eliminates the "curse of dimensionality". The estimators perform well in Monte Carlo experiments.

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Bibliographic Info

Paper provided by Carleton - School of Public Administration in its series Papers with number 99-25.

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Length: 42 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:fth:carlad:99-25

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Postal: Carleton University, School of Public Administration, Ottawa, Canada K1S 5B6.
Web page: http://www.carleton.ca/sppa/
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Keywords: SEMIPARAMETRIC MODELS ; EVALUATION;

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References

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  1. Tue Gorgens & Joel L. Horowitz, 1996. "Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable," Econometrics 9603001, EconWPA.
  2. 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.
  3. Hardle, W.K. & Tsybakov, A.B., 1992. "How sensitive are average derivatives?," Discussion Paper 1992-8, Tilburg University, Center for Economic Research.
  4. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  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. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
  7. Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
  8. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  9. Chunrong Ai, 1997. "A Semiparametric Maximum Likelihood Estimator," Econometrica, Econometric Society, vol. 65(4), pages 933-964, July.
  10. Oliver LINTON, . "Kernel estimation in a nonparametric marker dependent Hazard Model," Statistic und Oekonometrie 9313, Humboldt Universitaet Berlin.
  11. Joel L. Horowitz, 1999. "Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 67(5), pages 1001-1028, September.
  12. Haerdle,Wolfgang & Stoker,Thomas, 1987. "Investigations smooth multiple regression by the method of average derivatives," Discussion Paper Serie A 107, University of Bonn, Germany.
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