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.
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 language | English |
|---|---|
| Number of pages | 25 |
| Journal | Journal of Econometrics |
| Early online date | 5 Mar 2020 |
| DOIs | |
| Publication status | E-pub ahead of print - 5 Mar 2020 |
Structured keywords
- ECON Econometrics
- ECON CEPS
Keywords
- Covariate effects; duration analysis; kernel estimation; mortality; semiparametric estimation
Fingerprint Dive into the research topics of 'A General Semiparametric Approach to Inference with Marker-Dependent Hazard Rate Models'. Together they form a unique fingerprint.
Cite this
van den Berg, G. J., Mammen, E., Janys, L., & Nielsen, J. P. (2020). A General Semiparametric Approach to Inference with Marker-Dependent Hazard Rate Models. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2019.05.025