Blended identification in structural VARs

• 成果类型: Article
• 署名作者: Carriero, Andrea; Marcellino, Massimiliano; Tornese, Tommaso
• 署名单位: University of London; Queen Mary University London; Bocconi University
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• DOI: 10.1016/j.jmoneco.2024.103581
• 发表日期: 2024
• 关键词: SVAR; identification; heteroskedasticity; sign restrictions; Proxy variables

摘要

The proposed blended approach combines identification via heteroskedasticity with sign/ narrative restrictions, and instrumental variables. Since heteroskedasticity can point identify shocks, its use results in a sharp reduction of the potentially large identified sets stemming from other approaches. Conversely, sign/narrative restrictions or instrumental variables offer natural solutions to the labeling problem and can help when conditions for point identification through heteroskedasticity are not met. Blending these methods together resolves their respective key issues and leverages their advantages. We illustrate the benefits of the approach in Monte Carlo experiments, and apply it to several examples taken from the literature.