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.