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Semiparametric Estimation of Single-Index Transition Intensities

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  • Tue Gorgens

    (University of New South Wales)

Abstract

This research develops semiparametric kernel-based estimators of state-specific conditional transition intensities, h(y|x), for duration models with right-censoring and/or multiple destinations (competing risks). Both discrete and continuous duration data are considered. The maintained assumption is that h(y|x) depends on x only through an index x'b. In contrast to existing semiparametric estimators, proportional intensities is not assumed. The new estimators are asymptotically normally distributed. The estimator of b is root-n consistent. The estimator of h(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 Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 0596.

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Date of creation: 01 Aug 2000
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Handle: RePEc:ecm:wc2000:0596

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  1. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  2. Joel L. Horowitz, 1999. "Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 67(5), pages 1001-1028, September.
  3. J. L. HOROWITZ & Wolfgang HÄRDLE, 1994. "Direct Semiparametric Estimation of Single - Index Models with Discrete Covariates," SFB 373 Discussion Papers 1994,36, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  4. Hardle, W.K. & Tsybakov, A.B., 1992. "How sensitive are average derivatives?," Discussion Paper 1992-8, Tilburg University, Center for Economic Research.
  5. Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
  6. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  7. Chunrong Ai, 1997. "A Semiparametric Maximum Likelihood Estimator," Econometrica, Econometric Society, vol. 65(4), pages 933-964, July.
  8. 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.
  9. Haerdle,Wolfgang & Stoker,Thomas, 1987. "Investigations smooth multiple regression by the method of average derivatives," Discussion Paper Serie A 107, University of Bonn, Germany.
  10. Horowitz, J. & Gorgens, T., 1995. "Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable," Working Papers 95-15, University of Iowa, Department of Economics.
  11. 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.
  12. Oliver LINTON, . "Kernel estimation in a nonparametric marker dependent Hazard Model," Statistic und Oekonometrie 9313, Humboldt Universitaet Berlin.
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