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Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
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 …
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
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 …