Working Paper: CEPR ID: DP12589
Authors: Brandyn Bok; Daniele Caratelli; Domenico Giannone; Argia Sbordone; Andrea Tambalotti
Abstract: 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 "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.
Keywords: Monitoring Economic Conditions; Business Cycle Analysis; High-Dimensional Data; Real-Time Data Flow
JEL Codes: C32; C53; C55; E3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| incoming data releases (C82) | GDP growth estimates (O49) |
| positive surprise in consumption data (D12) | upward revision in GDP growth forecasts (O49) |
| negative surprises in manufacturing data (L60) | downward adjustments in GDP growth forecasts (H68) |
| nowcasting model (C53) | GDP growth estimates (O49) |