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

• Verfasst von: Berg, Gerard J. van den [VerfasserIn]
• Verfasst von: Janys, Lena [VerfasserIn]
• Verfasst von: Mammen, Enno [VerfasserIn]
• Verfasst von: Nielsen, Jens Perch [VerfasserIn]
• Titel: A general semiparametric approach to inference with marker-dependent hazard rate models
• Verf.angabe: Gerard. J. van den Berg, Lena Janys, Enno Mammen, Jens Perch Nielsen
• Jahr: 2021
• Jahr des Originals: 2020
• Umfang: 25 S.
• Fussnoten: Available online 5 March 2020 ; Gesehen am 07.04.2021
• Titel Quelle: Enthalten in: Journal of econometrics
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1973
• Jahr Quelle: 2021
• Band/Heft Quelle: 221(2021), 1 vom: März, Seite 43-67
• DOI: doi:10.1016/j.jeconom.2019.05.025
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Berg, Gerard J. van den, 1962 - : A general semiparametric approach to inference with marker-dependent hazard rate models. - 2021
• Sach-SW: Covariate effects
• Sach-SW: Duration analysis
• Sach-SW: Kernel estimation
• Sach-SW: Mortality
• Sach-SW: Semiparametric estimation
• K10plus-PPN: 1753269946

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.