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Nowcasting with large Bayesian vector autoregressions

Jacopo Cimadomo (), Domenico Giannone (), Michele Lenza (), Francesca Monti () and Andrej Sokol ()

No 2453, Working Paper Series from European Central Bank

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. JEL Classification: E32, E37, C01, C33, C53

Keywords: Big Data; business cycles; forecasting; mixed frequencies; real time; scenario analysis (search for similar items in EconPapers)
Date: 2020-08
New Economics Papers: this item is included in nep-big, nep-ets, nep-for and nep-mac
Note: 352854
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