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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

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Identification Number: 10.1017/S0266466619000033

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: 01 Oct 2024 03:03
URI: http://eprints.lse.ac.uk/id/eprint/91491

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