Efficiency bounds for estimating linear functionals of nonparametric regression models with endogenous regressors

Let Y=μ∗(X)+ε, where μ∗ is unknown and E[ε|X]≠0 with positive probability but there exist instrumental variables W such that E[ε|W]=0 w.p.1. It is well known that such nonparametric regression models are generally “ill-posed” in the sense that the map from the data to μ∗ is not continuous. In this p...

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Published inJournal of econometrics Vol. 170; no. 2; pp. 491 - 498
Main Authors Severini, Thomas A., Tripathi, Gautam
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.10.2012
Elsevier Sequoia S.A
Subjects
Branch & bound algorithms
Estimation
Linear equations
Linear models
Probability
Regression analysis
Studies
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Summary:Let Y=μ∗(X)+ε, where μ∗ is unknown and E[ε|X]≠0 with positive probability but there exist instrumental variables W such that E[ε|W]=0 w.p.1. It is well known that such nonparametric regression models are generally “ill-posed” in the sense that the map from the data to μ∗ is not continuous. In this paper, we derive the efficiency bounds for estimating certain linear functionals of μ∗ without assuming μ∗ itself to be identified.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2012.05.018