Over-identified Doubly Robust identification and estimation

A-Tier
Journal: Journal of Econometrics
Year: 2023
Volume: 235
Issue: 1
Pages: 25-42

Authors (3)

Lewbel, Arthur (Boston College) Choi, Jin Young (not in RePEc) Zhou, Zhuzhu (not in RePEc)

Score contribution per author:

1.341 = (α=2.01 / 3 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Consider two parametric models. At least one is correctly specified, but we do not know which. Both models include a common vector of parameters. An estimator for this common parameter vector is called Doubly Robust (DR) if it is consistent no matter which model is correct. We provide a general technique for constructing DR estimators (assuming the models are over identified). Our Over-identified Doubly Robust (ODR) technique is a simple extension of the Generalized Method of Moments. We illustrate our ODR with a variety of models. Our empirical application is instrumental variables estimation, where either one of two instrument vectors might be invalid.

Technical Details

RePEc Handle
repec:eee:econom:v:235:y:2023:i:1:p:25-42
Journal Field
Econometrics
Author Count
3
Added to Database
2026-01-25