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Non-parametric identication of the mixed proportional hazards model with interval-censored durations

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Abstract

This note presents identication results for the mixed proportional hazards model when duration data are interval-censored. Earlier positive results on identication under intervalcensoring require both parametric specication on how covariates enter the hazard functions and assumptions of unbounded support for covariates. New results provided here show how one can dispense with both of these assumptions. The mixed proportional hazards model is non-parametrically identied with interval-censored duration data, provided covariates have support on an open set and the hazard function is a non-constant continuous function of covariates.

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Paper provided by Research Department of Statistics Norway in its series Discussion Papers with number 600.

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Date of creation: Dec 2009
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Handle: RePEc:ssb:dispap:600

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Keywords: duration analysis; interval-censoring; non-parametric identication;

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  1. Elbers, Chris & Ridder, Geert, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," Review of Economic Studies, Wiley Blackwell, vol. 49(3), pages 403-09, July.
  2. Christian N. Brinch, 2008. "Non-parametric Identification of the Mixed Hazards Model with Interval-Censored Durations," Discussion Papers 539, Research Department of Statistics Norway.
  3. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The identifiability of the mixed proportional hazards competing risks model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 701-710.
  4. Sokbae 'Simon' Lee, 2005. "Identification of a competing risks model with unknown transformations of latent failure times," CeMMAP working papers CWP17/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. McCall, Brian P, 1994. "Testing the Proportional Hazards Assumption in the Presence of Unmeasured Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(3), pages 321-34, July-Sept.
  6. Heckman, J & Singer, B, 1984. "The Identifiability of the Proportional Hazard Model," Review of Economic Studies, Wiley Blackwell, vol. 51(2), pages 231-41, April.
  7. Sueyoshi, Glenn T, 1995. "A Class of Binary Response Models for Grouped Duration Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 411-31, Oct.-Dec..
  8. van den Berg, Gerard J & van Ours, Jan C, 1994. "Unemployment Dynamics and Duration Dependence in France, the Netherlands and the United Kingdom," Economic Journal, Royal Economic Society, vol. 104(423), pages 432-43, March.
  9. Han, Aaron & Hausman, Jerry A, 1990. "Flexible Parametric Estimation of Duration and Competing Risk Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(1), pages 1-28, January-M.
  10. Brinch, Christian N., 2007. "Nonparametric Identification Of The Mixed Hazards Model With Time-Varying Covariates," Econometric Theory, Cambridge University Press, vol. 23(02), pages 349-354, April.
  11. Knut R�ed & Tao Zhang, 2002. "A note on the Weibull distribution and time aggregation bias," Applied Economics Letters, Taylor & Francis Journals, vol. 9(7), pages 469-472.
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Cited by:
  1. Paolo Lucchino & Dr Richard Dorsett, 2013. "Young people's labour market transitions: the role of early experiences," NIESR Discussion Papers 11735, National Institute of Economic and Social Research.

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