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Macroeconomic Nowcasting and Forecasting with Big Data

Author

Listed:
  • Brandyn Bok

    (Federal Reserve Bank of New York, New York, New York 10045, USA)

  • Daniele Caratelli

    (Department of Economics, Stanford University, Stanford, California 94305, USA)

  • Domenico Giannone

    (Federal Reserve Bank of New York, New York, New York 10045, USA)

  • Argia M. Sbordone

    (Federal Reserve Bank of New York, New York, New York 10045, USA)

  • Andrea Tambalotti

    (Federal Reserve Bank of New York, New York, New York 10045, USA)

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 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.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.

Suggested Citation

  • Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
  • Handle: RePEc:anr:reveco:v:10:y:2018:p:615-643
    DOI: 10.1146/annurev-economics-080217-053214
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    More about this item

    Keywords

    business cycle analysis; high-dimensional data; monitoring economic conditions; real-time data flow;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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