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
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)
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Journal Article: Nowcasting with large Bayesian vector autoregressions (2022)
Working Paper: Nowcasting with Large Bayesian Vector Autoregressions (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20202453
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