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Sensitivity Analysis using Approximate Moment Condition Models

Timothy B. Armstrong, Michal Kolesár

arXiv 22 Aug 2018 · Econometrics · 29 citations (OpenAlex)

arXiv:1808.07387 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

51
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Andrews, I., Gentzkow, M., and Shapiro, J. M (2017) Measuring the sensitivity of parameter estimates to sample statistics1.000125100%
2Conley, T. G., Hansen, C. B., and Rossi, P. E (2012) Plausibly exogenous1.00053100%
3Kitamura, Y., Otsu, T., and Evdokimov, K (2013) Robustness, infinitesimal neighborhoods, and moment restrictions0.87472100%
4Newey, W. K. and McFadden, D. L (1994) Large sample estimation and hypothesis testing0.8434475%
5van der Vaart, A. W (1998) Asymptotic Statistics0.8435460%
6Donoho, D. L (1994) Statistical estimation and optimal recovery0.84333100%
7Imbens, G. W. and Manski, C. F (2004) Confidence intervals for partially identified parameters0.7373367%
8Newey, W. K (1985) Generalized method of moments specification testing0.7373367%
9DiTraglia, F. J (2016) Using invalid instruments on purpose: Focused moment selection and averaging for GMM0.73732100%
10Armstrong, T. B. and Kolesár, M (2018) Optimal inference in a class of regression models self0.65914329%

Showing the top 10 of 51 scored citations.

Cited by, within the corpus

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Citing paperIntensityMentionsSections
1Confidence intervals for intentionally biased estimators1.00064
2Plausible GMM: A Quasi-Bayesian Approach0.950145
3Robust Inference in Locally Misspecified Bipartite Networks0.87492
4True and Pseudo-True Parameters0.87462
5Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence0.84333
6Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs This version: April 22, 20240.81142
7The purpose of an estimator is what it does: Misspecification, estimands, and over-identification0.64432
8Structural models for policy-making0.64422
9A Misuse of Specification Tests0.64422
10Double Robustness of Local Projections and Some Unpleasant VARithmetic0.64422