2022
DOI: 10.1002/jae.2903
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Nowcasting tail risk to economic activity at a weekly frequency

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 Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. 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 dat… Show more

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Cited by 31 publications
(23 citation statements)
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“…In all cases, the priors' means are 0; the differences are related to the prior variances. In the first approach (labeled BQR-MN below), following studies such as Carriero, Clark, and Marcellino (2022), we fix the prior variance using a Minnesota-style form that takes account of the relative scales of variables and shrinks the coefficients on other variables more than those on the lag of the dependent variable. The shrinkage is controlled by two hyperparameters (smaller numbers mean more shrinkage): λ 1 , which controls the overall rate of shrinkage, and λ 2 , which controls the rate of shrinkage on variables other than lags of the dependent variable.…”
Section: Bayesian Quantile Regression (Bqr)mentioning
confidence: 99%
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“…In all cases, the priors' means are 0; the differences are related to the prior variances. In the first approach (labeled BQR-MN below), following studies such as Carriero, Clark, and Marcellino (2022), we fix the prior variance using a Minnesota-style form that takes account of the relative scales of variables and shrinks the coefficients on other variables more than those on the lag of the dependent variable. The shrinkage is controlled by two hyperparameters (smaller numbers mean more shrinkage): λ 1 , which controls the overall rate of shrinkage, and λ 2 , which controls the rate of shrinkage on variables other than lags of the dependent variable.…”
Section: Bayesian Quantile Regression (Bqr)mentioning
confidence: 99%
“…These include Bayesian quantile regression as developed in sources such as Khare and Hobert (2012) and Yu and Moyeed (2001) and used in macroeconomic forecasting by studies including Korobilis (2017) and Mitchell, Poon, and Mazzi (2022). Studies have also considered averaging or combining quantile forecasts (e.g., Giacomini and Komunjer (2005) and Korobilis (2017)) and factor reduction (e.g., Carriero, Clark, and Marcellino (2022)) through the partial quantile regression approach developed in Giglio, Kelly, and Pruitt (2016).…”
Section: Introductionmentioning
confidence: 99%
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“…As an extension to this it is sometimes useful to make predictions based on lagged observations when current data or recent data is lacking. This prediction is termed nowcasting 1,2 . It has been applied extensively in economic research and is now being adopted in infectious disease epidemiology for making health outcome predictions 3. .…”
Section: Assessing the Effects Of Np Interventionsmentioning
confidence: 99%