Minimizing Sensitivity to Model Misspecification

Stéphane Bonhomme and Martin Weidner

Papers from arXiv.org

Abstract: We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one-step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross-sectional binary choice model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified.

Date: 2018-07, Revised 2021-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (16)

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Related works:
Working Paper: Minimizing sensitivity to model misspecification (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1807.02161

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