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 diï¬?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 ï¬?nancial indicators in addition to a base set of macroeconomic indicators.