| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP17512 |
| DP17512 Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions | |
| Andrea Carriero; Todd Clark; Massimiliano Marcellino | |
| 发表日期 | 2022-07-28 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | A rapidly growing body of research has examined tail risks in macroeconomic outcomes, commonly using quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. This pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and variances (up). We show that Bayesian vector autoregressions (BVARs) with stochastic volatility are able to capture tail risks in macroeconomic forecast distributions and outcomes. Even though the 1-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, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Forecasting Downside risk Asymmetries |
| URL | https://cepr.org/publications/dp17512 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/546597 |
| 推荐引用方式 GB/T 7714 | Andrea Carriero,Todd Clark,Massimiliano Marcellino. DP17512 Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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