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
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Cited by:
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
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Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Achten, Sandra & Lessmann, Christian, 2020.
"Spatial inequality, geography and economic activity,"
World Development, Elsevier, vol. 136(C).
- Sandra Achten & Christian Lessmann, 2019. "Spatial inequality, geography and economic activity," CESifo Working Paper Series 7547, CESifo.
- 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).
- Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
- 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).
- 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.
- 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.
- 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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