DP15854 Nowcasting with Large Bayesian Vector Autoregressions
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.
