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Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions

  • Rothe, Christoph


    (Columbia University)

  • Firpo, Sergio


    (Sao Paulo School of Economics)

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.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 7564.

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Length: 44 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:iza:izadps:dp7564
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  1. Matias D. Cattaneo & Richard K. Crump & Michael Jansson, 2013. "Generalized Jackknife Estimators of Weighted Average Derivatives," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1243-1256, December.
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  15. repec:oup:qjecon:v:120:y:2005:i:3:p:1031-1083 is not listed on IDEAS
  16. Hidehiko Ichimura & Sokbae Lee, 2010. "Characterization of the asymptotic distribution of semiparametric M-estimators," Post-Print peer-00741628, HAL.
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