Minimizing Sensitivity to Model Misspecification
SpeakerMartin Weidner (University College London)
LocationUvA - E-building, Roetersstraat 11, Room E5.22
Date and time
March 29, 2019
16:00 - 17:15
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.
Joint with Stéphane Bonhomme.
Read full paper here.