Nonparametric identification of the mixed hazard model using martingale-based moments

Ruf, Johannes ORCID: 0000-0003-3616-2194 and Wolter, James Lewis (2020) Nonparametric identification of the mixed hazard model using martingale-based moments. Econometric Theory, 36 (2). 331 - 346. ISSN 0266-4666

Text - Accepted Version Download (539kB)

• Author

Abstract

Nonparametric identification of the Mixed Hazard model is shown. The setup allows for covariates that are random, time-varying, satisfy a rich path structure and are censored by events. For each set of model parameters, an observed process is constructed. The process corresponding to the true model parameters is a martingale, the ones corresponding to incorrect model parameters are not. The unique martingale structure yields a family of moment conditions that only the true parameters can satisfy. These moments identify the model and suggest a GMM estimation approach. The moments do not require use of the hazard function.

Item Type:	Article
Official URL:	https://www.cambridge.org/core/journals/econometri...
Additional Information:	© 2019 Cambridge University Press
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics
Date Deposited:	03 Jan 2019 16:54
Last Modified:	11 Dec 2024 21:46
URI:	http://eprints.lse.ac.uk/id/eprint/91491

Actions (login required)

View Item