Identification and Inference Under Narrative Restrictions

• Tác giả: Raffaella Giacomini, Toru Kitagawa, Matthew Read
• Ngôn ngữ: eng
• Ký hiệu phân loại: 820.923 English and Old English (Anglo-Saxon) literatures
• Thông tin xuất bản: 2021
• Bộ sưu tập: Báo, Tạp chí
• ID: 166230

Tóm tắt

 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 under 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.