Minimizing Sensitivity to Model Misspecification
By Martin P. Weidner, S. Bonhomme. Quantitative Economics, 2018
70 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/88c7b66aa4e5e005e6430f477e439a94524812aa
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In Economics and Econometrics
Citations in the corpus
- Sensitivity analysis using approximate moment condition models (2018, background): “In independent work, Bonhomme and Weidner (2020) provide a framework for estimation and inference in misspecified likelihood models when the misspecification set C is defined with respect to a larger class of models using statistical notions of distance.”