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Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators

In: Big Data for Twenty-First-Century Economic Statistics

Author

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  • Jeffrey C. Chen
  • Abe Dunn
  • Kyle Hood
  • Alexander Driessen
  • Andrea Batch

Abstract

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Suggested Citation

  • 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 Twenty-First-Century Economic Statistics, pages 373-402, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14268
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    References listed on IDEAS

    as
    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. 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.
    4. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    5. Rickard Nyman & Paul Ormerod, 2017. "Predicting Economic Recessions Using Machine Learning Algorithms," Papers 1701.01428, arXiv.org.
    6. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    7. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    8. 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).
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    11. Alberto Cavallo & W. Erwin Diewert & Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2018. "Using Online Prices for Measuring Real Consumption across Countries," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 483-487, May.
    12. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    13. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    14. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
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    Cited by:

    1. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    2. Achten, Sandra & Lessmann, Christian, 2020. "Spatial inequality, geography and economic activity," World Development, Elsevier, vol. 136(C).
    3. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    4. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    5. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
    6. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
    7. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
    8. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

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    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

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