DP15854 Nowcasting with Large Bayesian Vector Autoregressions
|Author(s):||Jacopo Cimadomo, Domenico Giannone, Michele Lenza, Francesca Monti, Andrej Sokol|
|Publication Date:||February 2021|
|Keyword(s):||Big Data, business cycles, Mixed frequency, Nowcasting, Real time, Scenario analysis|
|JEL(s):||C01, C33, C53, E32, E37|
|Programme Areas:||Monetary Economics and Fluctuations|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=15854|
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.