Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241912 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP37/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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 simple one-step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. 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 two binary choice models: a cross-sectional model where the error distribution is misspecified, and a dynamic panel data model where the number of time periods is small and the distribution of individual effects is misspecified.
Subjects: 
Model misspecification
robustness
sensitivity analysis
latent variables
JEL: 
C13
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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