The asymptotic efficiency of semiparametric estimators for censored linear regression models

1988 ◽  
Vol 13 (3-4) ◽  
pp. 123-140 ◽  
Author(s):  
J. L. Horowitz
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Asymptotic Efficiency ◽  
Linear Regression Models ◽  
Semiparametric Estimators ◽  
Censored Linear Regression

SEMIPARAMETRIC EFFICIENCY FOR CENSORED LINEAR REGRESSION MODELS WITH HETEROSKEDASTIC ERRORS

2017 ◽  
Vol 34 (1) ◽  
pp. 228-245
Author(s):  
Tao Chen
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Semiparametric Efficiency ◽  
Simplified Approach ◽  
Partially Linear ◽  
Finite Dimensional ◽  
Dimensional Parameters ◽  
Censored Linear Regression ◽  
Semiparametric Efficiency Bound

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.

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