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Thursday, October 28, 2021 at 11:15am to 12:45pmVirtual Event
Raffaella Giacomini, University College, London
Abstract: We consider structural vector autoregressions subject to ‘narrative restrictions’, which are inequality restrictions on functions of the structural shocks in speciﬁc periods. These restrictions raise novel problems related to identiﬁcation and inference, and there is currently no frequentist procedure for conducting inference in these models. We propose a solution that is valid from both Bayesian and frequentist perspectives by: 1) formalizing the identiﬁcation problem un-der narrative restrictions; 2) correcting a feature of the existing (single-prior) Bayesian approach that can distort inference; 3) proposing a robust (multiple-prior) Bayesian approach that is useful for assessing and eliminating the posterior sensitivity that arises in these models due to the likelihood having ﬂat regions; and 4) showing that the robust Bayesian approach has asymptotic frequentist validity. We illustrate our methods by estimating the eﬀects of US monetary policy under a variety of narrative restrictions.
If you are interested in participating in this seminar, please email Ulrike Kroeller at email@example.com for Zoom information.