Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229067
Authors: 
Cimadomo, Jacopo
Giannone, Domenico
Lenza, Michele
Monti, Francesca
Sokol, Andrej
Year of Publication: 
2020
Series/Report no.: 
ECB Working Paper No. 2453
Abstract: 
Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three "Vs": the large number of time series continuously released (Volume), the complexity of the data covering various sectors of the economy, published in an asynchronous way and with different frequencies and precision (Variety), and the need to incorporate new information within minutes of their release (Velocity). In this paper, we explore alternative routes to bring Bayesian Vector Autoregressive (BVAR) models up to these challenges. We find that BVARs are able to effectively handle the three Vs and produce, in real time, accurate probabilistic predictions of US economic activity and, in addition, a meaningful narrative by means of scenario analysis.
Subjects: 
Big Data
Scenario Analysis
Mixed Frequencies
Real Time
Business Cycles
Forecasting
JEL: 
E32
E37
C01
C33
C53
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4370-3
Document Type: 
Working Paper

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