Nowcasting Tail Risk to Economic Activity at a Weekly Frequency

Massimiliano Marcellino, Todd Clark and Andrea Carriero

No 16496, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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 different 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.

Keywords: Forecasting; Downside risk; Pandemics; Big data; Mixed frequency; Quantile regression (search for similar items in EconPapers)
JEL-codes: C53 E17 E37 F47 (search for similar items in EconPapers)
Date: 2021-08
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Journal Article: Nowcasting tail risk to economic activity at a weekly frequency (2022)
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