Nowcasting tail risk to economic activity at a weekly frequency
研究如何利用月度与周度数据，通过贝叶斯混合频率回归和分位数回归，每周更新对GDP增长尾部风险的预测，发现更多数据能提升预测准确性，且月度数据比周度数据更重要。
Summary This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators.