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Efficiency Bounds for Estimating Linear Functionals of Nonparametric Regression Models with Endogenous Regressors

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Author Info
Thomas A. Severini (Northwestern University)
Gautam Tripathi (University of Connecticut)

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Abstract

Consider a nonparametric regression model Y=mu*(X) + e, where the explanatory variables X are endogenous and e satisfies the conditional moment restriction E[e|W]=0 w.p.1 for instrumental variables W. It is well known that in these models the structural parameter mu* is 'ill-posed' in the sense that the function mapping the data to mu* is not continuous. In this paper, we derive the efficiency bounds for estimating linear functionals E[p(X)mu*(X)] and int_{supp(X)}p(x)mu*(x)dx, where p is a known weight function and supp(X) the support of X, without assuming mu* to be well-posed or even identified.

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Publisher Info
Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2007-18.

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Length: 23 pages
Date of creation: May 2007
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Handle: RePEc:uct:uconnp:2007-18

Note: We thank Gary Chamberlain, Enno Mammen, Whitney Newey, and participants at several seminars for helpful suggestions and conversations. The first author also thanks the NSF for financial support.
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Related research
Keywords: Efficiency bounds; Linear functionals; Nonparametric regression; Endogenous regressors;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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