Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

• 成果类型: Article
• 署名作者: Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano; Mertens, Elmar
• 署名单位: University of London; Queen Mary University London; Federal Reserve System - USA; Federal Reserve Bank - Cleveland; Bocconi University; Deutsche Bundesbank
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01213
• 发表日期: 2024-09
• 页码: 1403-1417
• 关键词: fat-tails; inference; models

摘要

The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To address these issues, we propose BVAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard BVARs. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best fit for the pandemic period, as well as for earlier subsamples of high volatility. In historical forecasting, outlier-augmented SV schemes fare at least as well as a conventional SV model.