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: Newcastle University
Enno Mammen: Heidelberg University
Jens P. Nielsen: Cass Business School

No 8339, IZA Discussion Papers from Institute of Labor Economics (IZA)

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: birth weight; covariate effects; survival analysis; local linear estimation; asymptotic distribution; mortality; social class (search for similar items in EconPapers)
JEL-codes: C14 C41 I12 J13 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2014-07
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published - published in: Journal of Econometrics, 2021, 221, 43-67

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Related works:
Working Paper: A general semiparametric approach to inference with marker-dependent hazard rate models (2016)
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