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

  • Johannes Ruf (a1) and James Lewis Wolter (a2)
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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Corresponding author
*Address correspondence to James Lewis Wolter, Lord, Abbett & Co. LLC, 90 Hudson Street, Jersey City, NJ 07302, USA; e-mail: jwolter@lordabbett.com.
Footnotes
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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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References
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Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
  • URL: /core/journals/econometric-theory
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