A general semiparametric approach to inference with marker-dependent hazard rate models
Gerard van den Berg,
Lena Janys,
Enno Mammen and
Jens P. Nielsen
Additional contact information
Lena Janys: University of Bonn
Enno Mammen: Heidelberg University and Higher School of Economics, Moscow
Jens P. Nielsen: Cass Business School, London
No 2016:3, Working Paper Series from IFAU - Institute for Evaluation of Labour Market and Education Policy
Abstract:
We examine a new general class of hazard rate models for survival data, containing a parametric and a nonparametric component. Both can be a mix of a time effect and (possibly time-dependent) marker or covariate effects. A number of well-known models are special cases. In a counting process framework, a general profile likelihood estimator is developed and the parametric component of the model is shown to be asymptotically normal and efficient. The analysis improves on earlier results for special cases. Finite sample properties are investigated in simulations. The estimator is shown to work well under realistic empirical conditions. The estimator is applied to investigate the long-run relationship between birth weight and later-life mortality using data from the Uppsala Birth Cohort Study of individuals born in 1915-1929. The results suggest a relationship that is difficult to capture with simple parametric specifications. Moreover, its shape at higher birth weights differs across gender.
Keywords: survival analysis; semiparametric estimation; covariate effects; kernel estimation; local linear regression; birth weight; mortality; Barker hypothesis; social class (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2016-02-28
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Related works:
Working Paper: A General Semiparametric Approach to Inference with Marker-Dependent Hazard Rate Models (2014) 
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:ifauwp:2016_003
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