DP16496 Nowcasting Tail Risk to Economic Activity at a Weekly Frequency
| Author(s): | Andrea Carriero, Todd Clark, Massimiliano Marcellino |
| Publication Date: | August 2021 |
| Keyword(s): | Big Data, Downside risk, Forecasting, Mixed frequency, Pandemics, Quantile regression |
| JEL(s): | C53, E17, E37, F47 |
| Programme Areas: | Monetary Economics and Fluctuations |
| Link to this Page: | cepr.org/active/publications/discussion_papers/dp.php?dpno=16496 |
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 di�erent models, consisting of Bayesian mixed frequency regressions with stochastic volatility, Bayesian quantile regressions, and Bayesian partial quantile regression, the last of which incorporates data reduction through a common factor. 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 �nancial indicators in addition to a base set of macroeconomic indicators.