Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions
研究发现贝叶斯向量自回归（BVAR）能有效捕捉产出增长、通胀和失业率的尾部风险，其预测表现与分位数回归相当，为宏观经济风险分析提供了新工具。
Abstract Many studies using quantile regressions (QRs) have found that downside risk to output growth varies more than upside risk. We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in forecast distributions. Even though the one‐step‐ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, forecasts of downside risks to output growth are more variable than upside risks, and the reverse applies in the case of inflation and unemployment. Overall, BVAR models perform comparably to QR for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.