Sensitivity Analysis using Approximate Moment Condition Models

Timothy Armstrong and Michal Koles'r
Additional contact information
Michal Koles'r: Princeton University

No 2158, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore differs from the one that is optimal under correct specification. To formally show the near-optimality of these CIs, we develop asymptotic efficiency bounds for inference in the locally misspecified GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more.

Keywords: GMM; Misspecification; Sensitivity analysis; Model selection (search for similar items in EconPapers)
JEL-codes: C10 C52 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2018-11
New Economics Papers: this item is included in nep-ore
Note: Includes Supplimental Material
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Sensitivity analysis using approximate moment condition models (2021)
Working Paper: Sensitivity Analysis using Approximate Moment Condition Models (2020)
Working Paper: Sensitivity Analysis using Approximate Moment Condition Models (2020)
Working Paper: Sensitivity Analysis using Approximate Moment Condition Models (2019)
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