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 modelswhere the distribution of individual effects may be misspecifiedand the number of time periods is small , and we revisit the structural evaluation of a conditional cash transfer program in Mexico .
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 .
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about the parameters of an economic model and predictions based on it,
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derive formulas to
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In addition, we provide
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both correct specification and
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We calibrate
To interpret
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using a model detection error approach
, we map it to the local power of a specification test of the reference model
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us to perform
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To illustrate our approach we study panel data modelswhere
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
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distribution of individual effects may be misspecifiedand the
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, and we revisit the structural evaluation of a conditional cash transfer program in Mexico
and the distribution of individual effects is misspecified