Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-03T03:26:13.470Z Has data issue: false hasContentIssue false

The Identifiability of the Mixed Proportional Hazards Model with Time-Varying Coefficients

Published online by Cambridge University Press:  11 February 2009

Brian P. McCall
Affiliation:
University of Minnesota

Abstract

This paper establishes conditions for the nonparametric identifiability of the mixed proportional hazards model with time-varying coefficients. Unlike the mixed proportional hazards model, a regressor with two distinct values is not sufficient to identify this model. An unbounded regressor, however, is sufficient for identification.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Burdett, K. (1979) Unemployment insurance payments as a search subsidy: A theoretical analysis. Economic Inquiry 17, 333343.CrossRefGoogle Scholar
Cane, V. (1977) A class of non-identifiable stochastic models. Journal of Applied Probability 14, 475482.CrossRefGoogle Scholar
Elbers, C. & Ridder, G. (1982) True and spurious duration dependence: The identifiability of the proportional hazards model. Review of Economic Studies 49, 403409.CrossRefGoogle Scholar
Feller, W. (1971) Introduction to Probability Theory and Its Applications, vol. II. New York: John Wiley and Sons.Google Scholar
Follmann, D., Goldberg, M., & May, L. (1990) Personal characteristics, unemployment insurance, and the duration of unemployment. Journal of Econometrics 45, 351366.CrossRefGoogle Scholar
Gore, S., Pocock, S., & Kerr, G. (1984) Regression models and non-proportional hazards in the analysis of breast cancer survival. Applied Statistics 33, 176195.CrossRefGoogle Scholar
Heckman, J. & Honore, B. (1989) The identifiability of the competing risks model. Biometrika 76, 325330.CrossRefGoogle Scholar
Heckman, J. & Singer, B. (1984a) A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52, 271320.CrossRefGoogle Scholar
Heckman, J. & Singer, B. (1984b) The identifiability of the proportional hazards model. Review of Economic Studies 51, 231241.CrossRefGoogle Scholar
Katz, L. & Meyer, B. (1991) The impact of the potential duration of unemployment benefits on the duration of unemployment. Journal of Public Economics 41, 4572.CrossRefGoogle Scholar
Kiefer, N. (1988) Economic duration data and hazard functions. Journal of Economic Literature 26, 646679.Google Scholar
Lancaster, T. (1979) Econometric methods for the duration of unemployment. Econometrica 47, 939956.CrossRefGoogle Scholar
Lancaster, T. (1990) The Econometric Analysis of Transition Data. New York: Cambridge University Press.CrossRefGoogle Scholar
Meyer, B. (1990) Unemployment insurance and unemployment spells. Econometrica 58, 757782.CrossRefGoogle Scholar
Mortensen, D. (1979) Unemployment insurance and job search outcomes. Industrial and Labor Relations Review 30, 595612.Google Scholar
Murphy, S. & Sen, P. (1990) Time-dependent coefficients in a Cox-type regression model. Stochastic Processes and Their Applications 39, 153180.CrossRefGoogle Scholar
Ridder, G. (1990) The non-parametric identification of generalized accelerated failure-time models. Review of Economic Studies 57, 167182.CrossRefGoogle Scholar
Stablein, D., Carter, W., & Novack, J. (1981) Analysis of survival data with nonproportional -hazard functions. Controlled Clinical trials 2, 149159.CrossRefGoogle ScholarPubMed
Zemanian, A. (1987) Distribution Theory and Transform Analysis. New York: Dover.Google Scholar