Nowcasting with large Bayesian vector autoregressions?

被引：27

作者：

Cimadomo, Jacopo ^{[1
]}

Giannone, Domenico ^{[2
,6
]}

Lenza, Michele ^{[1
,5
]}

Monti, Francesca ^{[3
,7
]}

Sokol, Andrej ^{[4
]}

机构：

[1] European Cent Bank, Frankfurt, Germany

[2] Amazon, Seattle, WA USA

[3] Catholic Univ Louvain, Ottignies Louvain la Neuv, Belgium

[4] Ctr Macroecon, London, England

[5] ULB, ECARES, Brussels, Belgium

[6] Univ Washington, Seattle, WA USA

[7] Kings Business Sch, London, England

来源：

JOURNAL OF ECONOMETRICS | 2022年 / 231卷 / 02期

关键词：

Big data; Scenario analysis; Mixed frequency; Real time; Business cycles; Nowcasting; REAL-TIME; MONETARY-POLICY; ECONOMIC-ACTIVITY; EURO AREA; MACROECONOMICS; MODEL; GDP;

D O I：

10.1016/j.jeconom.2021.04.012

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：500 / 519

页数：20

共 82 条

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