Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions

44 Pages Posted: 31 Aug 2013


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

JEL Classification: C14, C21, C31, C51

Suggested Citation

Rothe, Christoph and Firpo, Sergio, Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions. IZA Discussion Paper No. 7564. Available at SSRN:

Christoph Rothe (Contact Author)

Columbia University ( email )

Sergio Firpo

Insper ( email )

R Quata 300
Sao Paulo, 04542-030

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