Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189803 
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP59/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We propose a framework for estimation and inference about the parameters of an economic model and predictions based on it, 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 derive formulas to construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on simple one-step adjustments. We construct confidence intervals that contain the true parameter under both correct specification and local misspecification. We calibrate the degree of misspecification using a model detection error approach. Our approach allows us to perform systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. To illustrate our approach we study panel data models where the distribution of individual effects may be misspecified and the number of time periods is small, and we revisit the structural evaluation of a conditional cash transfer program in Mexico.
Subjects: 
Model misspecification
robustness
sensitivity analysis
structural models
counterfactuals
latent variables
panel data
JEL: 
C13
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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