Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions
Christoph Rothe and
No 7564, IZA Discussion Papers from Institute of Labor Economics (IZA)
We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step, but retain a fully nonparametric specification in the first step. Such estimators exist in many economic applications, including a wide range of missing data and treatment effect models. We show that these estimators are ?n-consistent and asymptotically normal under weaker than usual conditions on the accuracy of the first stage estimates, have smaller first order bias and second order variance, and that their finite-sample distribution can be approximated more accurately by classical first order asymptotics. We argue that because of these refinements our estimators are useful in many settings where semiparametric estimation and inference are traditionally believed to be unreliable. We also illustrate the practical relevance of our approach through simulations and an empirical application.
Keywords: semiparametric model; missing data; treatment effects; doubly robust estimation; higher order asymptotics (search for similar items in EconPapers)
JEL-codes: C14 C21 C31 C51 (search for similar items in EconPapers)
Pages: 44 pages
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Published in: Econometric Theory, 2019, 35 (5), 1048-1087
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Working Paper: Semiparametric estimation and inference using doubly robust moment conditions (2013)
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