Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/324438 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
cemmap working paper No. CWP14/25
Verlag: 
Centre for Microdata Methods and Practice (cemmap), The Institute for Fiscal Studies (IFS), London
Zusammenfassung: 
Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, verify that quasi-posteriors can be used to obtain approximately optimal Bayesian decision rules under the maintained prior structure over misspecification, and provide a form of frequentist coverage results. We illustrate the approach through empirical examples where we obtain informative inference for structural objects allowing for substantial relaxations of the requirement that moment conditions hold exactly
Schlagwörter: 
sensitivity analysis
misspecification
generalized method of moment (GMM)
quasi-Bayes
Bernstein-von Mises theorem (BvM)
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