Nowcasting Tail Risks to Economic Activity with Many Indicators
Todd Clark and
No 202013R2, Working Papers from Federal Reserve Bank of Cleveland
This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, as well as classical and Bayesian quantile regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). 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. The better-performing models or methods include the Bayesian regression model with stochastic volatility, Bayesian quantile regression, some approaches to data reduction that make use of factors, and forecast averaging. In contrast, simple quantile regression performs relatively poorly.
Keywords: pandemics; big data; quantile regression.; forecasting; downside risk; mixed frequency (search for similar items in EconPapers)
JEL-codes: C53 E17 E37 F47 (search for similar items in EconPapers)
Date: 2020-05-11, Revised 2020-09-22
New Economics Papers: this item is included in nep-big, nep-mac, nep-ore and nep-rmg
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