A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model

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

No 2012035, Norface Discussion Paper Series from Norface Research Programme on Migration, Department of Economics, University College London

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 one-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: mixed proportional hazard; linear rank estimation; counting process. (search for similar items in EconPapers)
JEL-codes: C14 C41 (search for similar items in EconPapers)
Date: 2012-11
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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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 Semi-Parametric Mixed Proportional Hazard Model (2009)
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