A simple GMM estimator for the semi-parametric mixed proportional hazard model

TY - UNPB

T1 - A simple GMM estimator for the semi-parametric mixed proportional hazard model

AU - Bijwaard, G.E.

AU - Ridder, G.

N1 - Reporting year: 2009

PY - 2009

Y1 - 2009

N2 - 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 Semi-Parametric 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.

AB - 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 Semi-Parametric 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.

M3 - Working paper/Discussion paper

VL - 4543

T3 - Discussion paper series

BT - A simple GMM estimator for the semi-parametric mixed proportional hazard model

PB - The Institute for the Study of Labor (IZA)

CY - Bonn

ER -