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NONPARAMETRIC IDENTIFICATION OF THE MIXED HAZARD MODEL USING MARTINGALE-BASED MOMENTS

Published online by Cambridge University Press:  20 February 2019

Johannes Ruf and
James Lewis Wolter
Johannes Ruf
Affiliation:
London School of Economics and Political Science
James Lewis Wolter*
Affiliation:
Lord, Abbett & Co. LLC
*
*Address correspondence to James Lewis Wolter, Lord, Abbett & Co. LLC, 90 Hudson Street, Jersey City, NJ 07302, USA; e-mail: jwolter@lordabbett.com.
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Abstract

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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.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 36 , Issue 2 , April 2020, pp. 331 - 346
Copyright
Copyright © Cambridge University Press 2019 

Footnotes

We thank an anonymous referee, Sokbae (Simon) Lee as the co-editor, and Peter Phillips as the editor for very helpful remarks that improved this paper. We are also grateful to the Oxford-Man Institute of Quantitative Finance for their hospitality.

References

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