ARE SMALL-SCALE SVARS USEFUL FOR BUSINESS CYCLE ANALYSIS? REVISITING NONFUNDAMENTALNESS

• 成果类型: Article
• 署名作者: Canova, Fabio; Hamidi Sahneh, Mehdi
• 署名单位: BI Norwegian Business School; Universidad Carlos III de Madrid
• 刊物名称: JOURNAL OF THE EUROPEAN ECONOMIC ASSOCIATION
• ISSN/ISSBN: 1542-4766
• DOI: 10.1093/jeea/jvx032
• 发表日期: 2018
• 页码: 1069-1093
• 关键词: vector autoregressions; INFORMATION; news; REPRESENTATIONS; models; shocks; DSGE

摘要

Nonfundamentalness arises when current and past values of the observables do not contain enough information to recover structural vector autoregressive (SVAR) disturbances. Using Granger causality tests, the literature suggested that several small-scale SVAR models are nonfundamental and thus not necessarily useful for business cycle analysis. We show that causality tests are problematic when SVAR variables cross-sectionally aggregate the variables of the underlying economy or proxy for nonobservables. We provide an alternative testing procedure, illustrate its properties with Monte Carlo simulations, and re-examine a prototypical small-scale SVAR model. (JEL: C5, C32, E5)