A general semiparametric approach to inference with marker-dependent hazard rate models

Gerard J. van den Berg, Lena Janys*, Enno Mammen, Jens Perch Nielsen

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

We examine a new general class of hazard rate models for duration data, containing a parametric and a nonparametric component. Both can be a mix of a time effect and possibly time-dependent 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. Finite sample properties are investigated in simulations. The estimator is applied to investigate the long-run relationship between birth weight and later-life mortality.

Original languageEnglish
Pages (from-to)43-67
Number of pages25
JournalJournal of Econometrics
Volume221
Issue number1
Early online date5 Mar 2020
DOIs
Publication statusPublished - Mar 2021

Funding

We thank the Associate Editor, three anonymous Referees, Nikolay Angelov and participants at conferences in Mannheim, Cambridge and Cologne and seminars in Bonn, Frankfurt, Bergen and Fribourg for their useful comments. We are grateful to CHESS (Stockholm University) and its UBCoS board members Ilona Koupil and Denny Vågerö for permission to use the UBCoS Multigen data. Gerard van den Berg, Enno Mammen and Lena Janys thank the German Science Foundation ( DFG ) for financial support through the FOR916 program. We thank Axel Munk for the coordination of this program. Enno Mammen’s research was supported by the DFG through the Research Training Group RTG 1953 and it was prepared at the National Research University Higher School of Economics, Moscow, Russian Federation within the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program. Lena Janys acknowledges funding through the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - GZ 2047/1 , Projekt-ID 390685813.

Keywords

  • Covariate effects
  • Duration analysis
  • Kernel estimation
  • Mortality
  • Semiparametric estimation

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