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A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model

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  • Govert Bijwaard

    ()
    (Netherlands Interdisciplinary Demographic Institute (NIDI))

  • Geert Ridder

    ()
    (University of Southern California)

  • Tiemen Woutersen

    (Johns Hopkins University)

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.

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Bibliographic Info

Paper provided by Norface Research Programme on Migration, Department of Economics, University College London in its series Norface Discussion Paper Series with number 2012035.

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Date of creation: Nov 2012
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Handle: RePEc:nor:wpaper:2012035

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

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  30. repec:cup:cbooks:9780521496032 is not listed on IDEAS
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