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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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    1. Martin Ellison & Sang Seok Lee & Kevin Hjortshøj O'Rourke, 2020. "The Ends of 30 Big Depressions," NBER Working Papers 27586, National Bureau of Economic Research, Inc.
    2. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    3. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    4. Lyu, Yifei & Nie, Jun & Yang, Shu-Kuei X., 2021. "Forecasting US economic growth in downturns using cross-country data," Economics Letters, Elsevier, vol. 198(C).
    5. , 2020. "Forecasting U.S. Economic Growth in Downturns Using Cross-Country Data," Research Working Paper RWP 20-09, Federal Reserve Bank of Kansas City.
    6. Christoph Görtz & Mallory Yeromonahos, 2019. "Asymmetries in Risk Premia, Macroeconomic Uncertainty and Business Cycles," CESifo Working Paper Series 7959, CESifo.
    7. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    8. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Sokol, Andrej & Monti, Francesca, 2020. "Nowcasting with large Bayesian vector autoregressions," Working Paper Series 2453, European Central Bank.
    9. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    10. Jeffrey C. Chen & Abe Dunn & Kyle Hood & Alexander Driessen & Andrea Batch, 2019. "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators," NBER Chapters, in: Big Data for 21st Century Economic Statistics, National Bureau of Economic Research, Inc.
    11. Alkhareif, Ryadh M. & Barnett, William A., 2020. "Nowcasting Real GDP for Saudi Arabia," MPRA Paper 104278, University Library of Munich, Germany.
    12. Jonas E. Arias & Minchul Shin, 2020. "Tracking U.S. Real GDP Growth During the Pandemic," Economic Insights, Federal Reserve Bank of Philadelphia, vol. 5(3), pages 9-14, September.
    13. Peter Fuleky, 2020. "Nowcasting the Trajectory of the COVID-19 Recovery," Working Papers 2020-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    14. David Kohns & Arnab Bhattacharjee, 2020. "Developments on the Bayesian Structural Time Series Model: Trending Growth," Papers 2011.00938, arXiv.org.
    15. Pérez, Fernando, 2018. "Nowcasting Peruvian GDP using Leading Indicators and Bayesian Variable Selection," Working Papers 2018-010, Banco Central de Reserva del Perú.
    16. Ryadh M. Alkhareif & William Barnett, 2020. "Nowcasting Real Gdp For Saudi Arabia," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202018, University of Kansas, Department of Economics, revised Nov 2020.
    17. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    18. Alifatussaadah, Ardiana & Primariesty, Anindya Diva & Soleh, Agus Mohamad & Andriansyah, Andriansyah, 2019. "Nowcasting Indonesia's GDP Growth: Are Fiscal Data Useful?," MPRA Paper 105252, University Library of Munich, Germany.
    19. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    20. Alexander James & Yaser S. Abu-Mostafa & Xiao Qiao, 2019. "Nowcasting Recessions using the SVM Machine Learning Algorithm," Papers 1903.03202, arXiv.org, revised Jun 2019.
    21. Bhadury, Soumya & Ghosh, Saurabh & Kumar, Pankaj, 2019. "Nowcasting GDP Growth Using a Coincident Economic Indicator for India," MPRA Paper 96007, University Library of Munich, Germany.

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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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