Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/107864 
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper No. 14-152/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
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 nowc asts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first 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.
Subjects: 
Density forecast combination
Survey forecast
Bayesian Filtering
Sequential Monte Carlo Nowcasting
Real-time Data
JEL: 
C11
C13
C32
C53
E37
Document Type: 
Working Paper

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