Combined Density Nowcasting in an uncertain economic environment
Knut Are Aastveit (),
Francesco Ravazzolo () and
Herman van Dijk
No 2014/17, Working Paper from Norges Bank
We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-time data of 120 leading indicators, indicate that CDN gives more accurate density nowcasts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two rst months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.
Keywords: Density forecast combination, Survey forecast, Bayesian filtering; Sequential Monte Carlo Nowcasting; Real-time data (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac, nep-ore and nep-upt
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Working Paper: Combined Density Nowcasting in an Uncertain Economic Environment (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:bno:worpap:2014_17
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