Locally Robust Semiparametric Estimation

Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney Newey and James M. Robins

Papers from arXiv.org

Abstract: Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where moment conditions have zero derivative with respect to first steps. We show that orthogonal moment functions can be constructed by adding to identifying moments the nonparametric influence function for the effect of the first step on identifying moments. Orthogonal moments reduce model selection and regularization bias, as is very important in many applications, especially for machine learning first steps. We give debiased machine learning estimators of functionals of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that adding to identifying moments the nonparametric influence function provides a general construction of orthogonal moments, including regularity conditions, and show that the nonparametric influence function is robust to additional unknown functions on which it depends. We give a general approach to estimating the unknown functions in the nonparametric influence function and use it to automatically debias estimators of functionals of high dimensional conditional location learners. We give a variety of new doubly robust moment equations and characterize double robustness. We give general and simple regularity conditions and apply these for asymptotic inference on functionals of high dimensional regression quantiles and dynamic discrete choice parameters with high dimensional state variables.

Date: 2016-07, Revised 2020-08
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (17)

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http://arxiv.org/pdf/1608.00033 Latest version (application/pdf)

Related works:
Journal Article: Locally Robust Semiparametric Estimation (2022)
Working Paper: Locally robust semiparametric estimation (2018)
Working Paper: Locally robust semiparametric estimation (2016)
Working Paper: Locally robust semiparametric estimation (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1608.00033

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