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NONPARAMETRIC IDENTIFICATION OF THE MIXED HAZARDS MODEL WITH TIME-VARYING COVARIATES

Published online by Cambridge University Press:  30 January 2007

Christian N. Brinch
Christian N. Brinch
Affiliation:
University of Oslo
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Abstract

Most nonparametric identification results for the mixed proportional hazards model for single spell duration data depend crucially on the proportional hazards assumption. Here, it is shown that variation in covariates over time, combined with variation across observations, is sufficient to ensure identification without the proportional hazards assumption. The required variation over time is minimal, and the mixed hazards model is identified without the proportional hazards assumption in the presence of standard time-varying covariates.Thanks to Kåre Bævre, Zhiyang Jia, Tore Schweder, Rolf Aaberge, and John K. Dagsvik for useful comments.

Type
MISCELLANEA
Information
Econometric Theory , Volume 23 , Issue 2 , April 2007 , pp. 349 - 354
Copyright
© 2007 Cambridge University Press

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References

REFERENCES

Cox, D.R. (1972) Regression models and life tables (with discussion). Journal of the Royal Statistical Society Series B 34, 187–220.Google Scholar
Elbers, C. & G. Ridder (1982) True and spurious duration dependence: The identifiability of the proportional hazards model. Review of Economic Studies 49, 402–411.Google Scholar
Feller, W. (1968) On Muntz' theorem and completely monotone functions. American Mathematics Monthly 75, 342–359.Google Scholar
Feller, W. (1971) An Introduction to Probability Theory and Its Applications, vol. 2. Wiley.
Heckman, J.J. (1991) Identifying the hand of past: Distinguishing state dependence from heterogeneity. American Economic Review 81(2), 75–79.Google Scholar
Heckman, J.J. & B. Singer (1984a) Econometric duration analysis. Journal of Econometrics 24, 63–132.Google Scholar
Heckman, J.J. & B. Singer (1984b) The identifiability of the proportional hazards model. Review of Economic Studies 51, 231–241.Google Scholar
Heckman, J.J. & C. Taber (1994) Econometric mixture models and more general models for unobservables in duration analysis. Statistical Methods in Medical Research 3, 279–299.Google Scholar
Honoré, B. (1991) Identification results for duration models with multiple spells or time-varying covariates. Working paper, Northwestern University.
Honoré, B. (1993) Identification results for duration models with multiple spells. Review of Economic Studies 60, 241–246.Google Scholar
Lancaster, T. (1979) Econometric methods for the duration of unemployment. Econometrica 47, 939–956.Google Scholar
Lancaster, T. & S. Nickel (1980) The analysis of reemployment probabilities for the unemployed. Journal of the Royal Statistical Society, Series A 143, 141–165.Google Scholar
McCall, B.P. (1994a) Identifying state dependence in durations models. In American Statistical Association 1994, Proceedings of the Business and Economics Section, 14–17. American Statistical Association.
McCall, B.P. (1994b) Testing the proportional hazards assumption in the presence of unmeasured heterogeneity. Journal of Applied Econometrics 9, 321–334.Google Scholar
McCall, B.P. (1996) The identifiability of the mixed proportional hazards model with time-varying coefficients. Econometric Theory 12, 733–738.Google Scholar
Ridder, G. (1990) The non-parametric identification of generalized accelerated failure time models. Review of Economic Studies 57, 167–182.Google Scholar
van den Berg, G. (2001) Duration models: Specification, identification, and multiple durations. In J.J. Heckman and E. Leamer (eds.), Handbook of Econometrics, vol. 5. North-Holland.