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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

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.

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

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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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