A Simple GMM Estimator for the Semi-Parametric Mixed Proportional Hazard Model

Govert Bijwaard and Geert Ridder ()
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Geert Ridder: University of Southern California

No 4543, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Ridder and Woutersen (2003) have shown that under a weak condition on the baseline hazard there exist root-N consistent estimators of the parameters in a semiparametric Mixed Proportional Hazard model with a parametric baseline hazard and unspecified distribution of the unobserved heterogeneity. We extend the Linear Rank Estimator (LRE) of Tsiatis (1990) and Robins and Tsiatis (1991) to this class of models. The optimal LRE is a two-step estimator. We propose a simple first-step estimator that is close to optimal if there is no unobserved heterogeneity. The efficiency gain associated with the optimal LRE increases with the degree of unobserved heterogeneity.

Keywords: counting process; linear rank estimation; mixed proportional hazard (search for similar items in EconPapers)
JEL-codes: C14 C41 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2009-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

Published - published in: Journal of Econometric Methods, 2013, 2, 1-23 [PDF]

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Related works:
Journal Article: A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model (2013)
Working Paper: A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model (2012)
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