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Nonparametric Identification Of The Mixed Hazard Model Using Martingale-Based Moments

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  • Ruf, Johannes
  • Wolter, James Lewis

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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  • Ruf, Johannes & Wolter, James Lewis, 2020. "Nonparametric Identification Of The Mixed Hazard Model Using Martingale-Based Moments," Econometric Theory, Cambridge University Press, vol. 36(2), pages 331-346, April.
    • Handle: RePEc:cup:etheor:v:36:y:2020:i:2:p:331-346_5
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