Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189871 
Year of Publication: 
2017
Series/Report no.: 
Staff Report No. 830
Publisher: 
Federal Reserve Bank of New York, New York, NY
Abstract: 
Data, data, data . . . Economists know it 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 "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 the 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.
Subjects: 
monitoring economic conditions
business cycle
macroeconomic data
large data sets
high-dimensional data
real-time data flow
factor model
state space models
Kalman filter
JEL: 
C32
C53
C55
E32
Document Type: 
Working Paper

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