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Nowcasting with large Bayesian vector autoregressions?
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AI
DOI：10.1016/j.jeconom.2021.04.012.png)
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Abstract
En 中文
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 con-tinuously 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.(c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Big data
Scenario analysis
Mixed frequency
Real time
Business cycles
Nowcasting
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