Macroeconomic nowcasting and forecasting with big data
Domenico Giannone (),
Argia Sbordone () and
Andrea Tambalotti ()
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Daniele Caratelli: Federal Reserve Bank of New York
No 830, Staff Reports from Federal Reserve Bank of New York
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.
Keywords: monitoring economic conditions; business cycle; macroeconomic data; large data sets; high-dimensional data; real-time data flow; factor model; state space models; Kalman filter (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-for and nep-mac
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Journal Article: Macroeconomic Nowcasting and Forecasting with Big Data (2018)
Working Paper: Macroeconomic Nowcasting and Forecasting with Big Data (2018)
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