Macroeconomic Nowcasting and Forecasting with Big Data

被引:59
|
作者
Bok, Brandyn [1 ]
Caratelli, Daniele [2 ]
Giannone, Domenico [1 ]
Sbordone, Argia M. [1 ]
Tambalotti, Andrea [1 ]
机构
[1] Fed Reserve Bank New York, New York, NY 10045 USA
[2] Stanford Univ, Dept Econ, Stanford, CA 94305 USA
来源
关键词
monitoring economic conditions; business cycle analysis; high-dimensional data; real-time data flow; DYNAMIC-FACTOR MODEL; REAL-TIME; LARGE NUMBER; GERMAN GDP; INDICATORS; COINCIDENT; SERIES; MIDAS; RUN;
D O I
10.1146/annurev-economics-080217-053214
中图分类号
F [经济];
学科分类号
02 ;
摘要
Data, data, data .... Economists know their importance well, especially when it comes to monitoring macroeconomic conditions-the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before so-called big data became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.
引用
收藏
页码:615 / 643
页数:29
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