Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296291 
Year of Publication: 
2022
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 13 [Issue:] 3 [Year:] 2022 [Pages:] 907-954
Publisher: 
The Econometric Society, New Haven, CT
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.
Subjects: 
Model misspecification
robustness
sensitivity analysis
JEL: 
C13
C23
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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