A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model
Govert Bijwaard (),
Ridder Geert and
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Ridder Geert: Department of Economics, Kaprielian Hall, Los Angeles, CA, USA
Woutersen Tiemen: Department of Economics, Eller College of Management, University of Arizona, Tucson, AZ, USA
Journal of Econometric Methods, 2013, vol. 2, issue 1, 1-23
Ridder and Woutersen (Ridder, G., and T. Woutersen. 2003. “The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model.” Econometrica 71: 1579–1589) 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 (Tsiatis, A. A. 1990. “Estimating Regression Parameters using Linear Rank Tests for Censored Data.” Annals of Statistics 18: 354–372) and Robins and Tsiatis (Robins, J. M., and A. A. Tsiatis. 1992. “Semiparametric Estimation of an Accelerated Failure Time Model with Time-Dependent Covariates.” Biometrika 79: 311–319) 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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Working Paper: A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model (2012)
Working Paper: A Simple GMM Estimator for the Semi-Parametric Mixed Proportional Hazard Model (2009)
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