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

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  • Bijwaard, Govert

    ()
    (NIDI - Netherlands Interdisciplinary Demographic Institute)

  • Ridder, Geert

    ()
    (University of Southern California)

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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4543.

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Length: 46 pages
Date of creation: Nov 2009
Date of revision:
Publication status: published in: Journal of Econometric Methods, 2013, 2, 1-23
Handle: RePEc:iza:izadps:dp4543

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Keywords: mixed proportional hazard; linear rank estimation; counting process;

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