Open AccessSemiparametric efficiency in nonlinear LATE models
Author(s) -
Hong Han,
Nekipelov Denis
Publication year - 2010
Publication title -
quantitative economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.062
H-Index - 27
eISSN - 1759-7331
pISSN - 1759-7323
DOI - 10.3982/qe43
Subject(s) - estimator , semiparametric model , semiparametric regression , parametric statistics , covariate , mathematics , monotonic function , conditional expectation , econometrics , nonlinear system , mathematical optimization , computer science , statistics , physics , quantum mechanics , mathematical analysis
In this paper we study semiparametric efficiency for the estimation of a finite‐dimensional parameter defined by generalized moment conditions under the local instrumental variable assumptions. These parameters identify treatment effects on the set of compliers under the monotonicity assumption. The distributions of covariates, the treatment dummy, and the binary instrument are not specified in a parametric form, making the model semiparametric. We derive the semiparametric efficiency bounds for both conditional models and unconditional models. We also develop multistep semiparametric efficient estimators that achieve the semiparametric efficiency bound. To illustrate the efficiency gains from using the optimal semiparametric weights, we design a Monte Carlo study. It demonstrates that our semiparametric estimator performs well in nonlinear models.