Nowcasting with large Bayesian vector autoregressions

Jacopo Cimadomo, Domenico Giannone, Michele Lenza, Francesca Monti and Andrej Sokol
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Francesca Monti: Université catholique de Louvain, LIDAM/CORE, Belgium

No 3331, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three ‘‘Vs’’. Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this paper, we explore three alternative routes to nowcasting with Bayesian Vector Autoregressive (BVAR) models and find that they can effectively handle the three Vs by producing, in real time, accurate probabilistic predictions of US economic activity and a meaningful narrative by means of scenario analysis.

Keywords: Big data; Scenario analysis; Mixed frequency; Real time; Business cycles; Nowcasting (search for similar items in EconPapers)
JEL-codes: C01 C33 C53 E32 E37 (search for similar items in EconPapers)
Pages: 20
Date: 2025-01-01
Note: In: Journal of Econometrics, 2022, vol. 231 (2), p. 500-519
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3331

DOI: 10.1016/j.jeconom.2021.04.012

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