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NONPARAMETRIC IDENTIFICATION OF THE MIXED HAZARD MODEL USING MARTINGALE-BASED MOMENTS
Published online by Cambridge University Press: 20 February 2019
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.
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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.
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