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SEMIPARAMETRIC EFFICIENCY FOR CENSORED LINEAR REGRESSION MODELS WITH HETEROSKEDASTIC ERRORS

Published online by Cambridge University Press:  01 February 2017

Tao Chen
Tao Chen*
Affiliation:
University of Waterloo
*
*Address correspondence to Tao Chen, Department of Economics, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1; e-mail: t66chen@uwaterloo.ca.
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Abstract

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Using a simplified approach developed by Severini and Tripathi (2001), we calculate the semiparametric efficiency bound for the finite-dimensional parameters of censored linear regression models with heteroskedastic errors. Under an additional identification at infinity type assumption, we propose an efficient estimator based on a novel result from Lewbel and Linton (2002). An extension to censored partially linear single-index models is also presented.

Information

Type
MISCELLANEA
Information
Econometric Theory , Volume 34 , Issue 1 , February 2018 , pp. 228 - 245
Copyright
Copyright © Cambridge University Press 2017 

Footnotes

The author is grateful to the editors and three anonymous referees for comments, and would like to thank Tom Parker and Gautam Tripathi for helpful discussions.

References

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