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How is machine learning useful for macroeconomic forecasting?

Citations

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Cited by:

  1. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
  2. Zin Mar Oo & Ching‐Yang Lin & Makoto Kakinaka, 2025. "Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1531-1562, July.
  3. Philippe Goulet Coulombe, 2024. "The macroeconomy as a random forest," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 401-421, April.
  4. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
  5. van Dijk Herman K., 2024. "Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 155-176, April.
  6. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
  7. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
  8. Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Dec 2025.
  9. Dewang Li & Chingfei Luo & Meilan Qiu, 2025. "Optimal Weighted Markov Model and Markov Optimal Weighted Combination Model with Their Application in Hunan’s Gross Domestic Product," Mathematics, MDPI, vol. 13(3), pages 1-19, February.
  10. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
  11. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
  12. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
  13. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Sep 2024.
    • Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
  14. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Zhao, Junyi, 2025. "Is machine learning a necessity? A regression-based approach for stock return prediction," Journal of Empirical Finance, Elsevier, vol. 81(C).
  15. McWilliams, William N. & Isengildina Massa, Olga & Stewart, Shamar L., 2024. "Annual Food Price Inflation Forecasting: A Macroeconomic Random Forest Approach," 2024 Annual Meeting, July 28-30, New Orleans, LA 343923, Agricultural and Applied Economics Association.
  16. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 39(3), pages 1145-1162.
  17. Mick van Rooijen & Dorinth W. van Dijk & Jasper M. de Winter, 2025. "Designing a Nowcasting Model for GDP Growth: A Practical Approach," De Economist, Springer, vol. 173(4), pages 665-704, December.
  18. Mihail Yanchev, 2025. "Interval, Quantile and Density Forecasts," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 109-129, March.
  19. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
  20. Richard Ashley & Randal Verbrugge, 2025. "The intermittent Phillips curve: Finding a stable (but persistence‐dependent) Phillips curve model specification," Economic Inquiry, Western Economic Association International, vol. 63(3), pages 926-944, July.
  21. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
  22. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
  23. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
  24. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
  25. repec:ags:aaea22:343923 is not listed on IDEAS
  26. Jairo Flores & Bruno Gonzaga & Walter Ruelas-Huanca & Juan Tang, 2025. "Nowcasting Peru's GDP with Machine Learning Methods," IHEID Working Papers 01-2025, Economics Section, The Graduate Institute of International Studies.
  27. William Rudd & Howard Bondell & Jeremy Silver, 2026. "Augmenting Neural Networks With Time‐Varying Weights," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 22-28, January.
  28. Dalibor Stevanovic, 2026. "Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure," Papers 2604.14467, arXiv.org.
  29. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2025. "Testing Clustered Equal Predictive Ability with Unknown Clusters," Papers 2507.14621, arXiv.org, revised Jul 2026.
  30. Sakai Ando & Taehoon Kim, 2024. "Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(4), pages 1386-1410, December.
  31. Pietro Bogani & Matteo Fontana & Luca Neri & Simone Vantini, 2024. "Calibrated quantile prediction for Growth-at-Risk," Papers 2411.00520, arXiv.org.
  32. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  33. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
  34. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
  35. 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).
  36. Andrea Carriero & Florian Huber & Davide Pettenuzzo, 2026. "Double Descent and Benign Overfitting in Macroeconomic Forecasting," Papers 2605.15358, arXiv.org.
  37. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
  38. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
  39. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
  40. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
  41. Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022. "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, vol. 46(PA).
  42. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
  43. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
  44. 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.
  45. Juan Jos√© Rinc√≥n Brice√±o, 2025. "Colombian economic activity nowcasting: addressing nonlinearities and high dimensionality through machine-learning," Documentos CEDE 21388, Universidad de los Andes, Facultad de Economía, CEDE.
  46. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2025. "Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables," CIRANO Working Papers 2025s-15, CIRANO.
  47. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
  48. Philippe Goulet Coulombe, 2026. "LGB+: A Macroeconomic Forecasting Road Test," Papers 2605.09740, arXiv.org.
  49. Goulet Coulombe, Philippe, 2025. "Time-varying parameters as ridge regressions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 982-1002.
  50. Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022. "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 379-405, February.
  51. Alina Landowska & Robert A. K{l}opotek & Dariusz Filip & Konrad Raczkowski, 2025. "GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest," Papers 2504.20993, arXiv.org.
  52. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
  53. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.
  54. BBVA Research & Alvaro Ortiz & Tomasa Rodrigo, 2025. "Global | Geopolítica, geoeconomía y riesgo: un enfoque basado en aprendizaje automático [Global | Geopolitics, geoeconomics and risk: a machine learning approach]," Working Papers 25/14, BBVA Bank, Economic Research Department.
  55. Sihan Tu & Zhaoxing Gao, 2025. "A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting," Papers 2502.15275, arXiv.org.
  56. Nikoleta Anesti & Eleni Kalamara & George Kapetanios, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England Staff Working Paper series 923, Bank of England.
  57. Ademmer, Martin & Beckmann, Joscha & Bode, Eckhardt & Boysen-Hogrefe, Jens & Funke, Manuel & Hauber, Philipp & Heidland, Tobias & Hinz, Julian & Jannsen, Nils & Kooths, Stefan & Söder, Mareike & Stame, 2021. "Big Data in der makroökonomischen Analyse," Kieler Beiträge zur Wirtschaftspolitik 32, Kiel Institute for the World Economy.
  58. Philippe Goulet Coulombe & Karin Klieber, 2025. "Opening the Black Box of Local Projections," Papers 2505.12422, arXiv.org, revised Jul 2025.
  59. A. Ford Ramsey & Michael K. Adjemian, 2024. "Forecast combination in agricultural economics: Past, present, and the future," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(4), pages 1450-1478, December.
  60. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
  61. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
  62. Manu Sharma & Vinish Kathuria, 2025. "Macroeconomic Nowcasting: What can Central Banks Learn from a Structured Literature Review?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 23(2), pages 333-388, June.
  63. Muhammad Anees Khan & Kumail Abbas & Mazliham Mohd Su’ud & Anas A. Salameh & Muhammad Mansoor Alam & Nida Aman & Mehreen Mehreen & Amin Jan & Nik Alif Amri Bin Nik Hashim & Roslizawati Che Aziz, 2022. "Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
  64. Johan Brannlund & Helen Lao & Maureen MacIsaac & Jing Yang, 2023. "Predicting Changes in Canadian Housing Markets with Machine Learning," Discussion Papers 2023-21, Bank of Canada.
  65. Firmin Ayivodji & Etienne Briand & Kevin Moran & Dalibor Stevanovic, 2026. "Monetary Policy in the Media Spotlight: Sentiments, Signals, and Economic Impact," Working Papers 26-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
  66. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
  67. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
  68. 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.
  69. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024. "Machine learning and economic forecasting: The role of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
  70. Chi, Ta-Chung & Fan, Ting-Han & Ghigliazza, Raffaele & Giannone, Domenico & Wang, Zixuan (Kevin), 2025. "Macroeconomic Forecasting and Machine Learning," CEPR Discussion Papers 20727, Centre for Economic Policy Research.
  71. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
  72. Jinbo Cai & Wenze Li & Wenjie Wang, 2025. "Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy," Papers 2507.07477, arXiv.org.
  73. Bolivar, Osmar, 2026. "High-frequency inflation forecasting: A two-step machine learning methodology," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 7(1).
  74. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2025. "Density forecasts of inflation: A quantile regression forest approach," European Economic Review, Elsevier, vol. 178(C).
  75. Nicolas Gavoille, 2025. "A short drop or a sudden stop? Sanctions, trade shocks, and firms' adjustment margins," Working Papers 2025/03, Latvijas Banka.
  76. Hanus, Luboš & Baruník, Jozef, 2025. "Learning the probability distributions of day-ahead electricity prices," Energy Economics, Elsevier, vol. 152(C).
  77. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
  78. Michael Scholz, 2025. "Forecast combinations for benchmarks of long-term stock returns using machine learning methods," Annals of Operations Research, Springer, vol. 352(3), pages 583-612, September.
  79. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
  80. Midha, Joshua, 2024. "Assessing Emerging Markets through Transactional Dynamics: A New Multi-Dimensional Valuation Framework," SocArXiv d8jkt, Center for Open Science.
  81. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023. "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, vol. 124(C).
  82. repec:ehl:lserod:128852 is not listed on IDEAS
  83. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  84. Thierry Warin & Sarah Elimam, 2025. "GDP 5.0: Real-Time, Micro-Founded and Sustainable Metrics for Beyond-GDP Economic Assessment," CIRANO Working Papers 2025s-20, CIRANO.
  85. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
  86. Philippe Goulet Coulombe, 2026. "Quantifying the Risk-Return Tradeoff in Forecasting," Papers 2605.09712, arXiv.org.
  87. Tu DQ Le & Son H Tran & Thanh Ngo & Hung D Bui, 2026. "Forecasting Vietnam Inflation Using Machine Learning Approaches: A Comprehensive Analysis," Advances in Decision Sciences, Asia University, Taiwan, vol. 30(1), pages 136-185.
  88. Yiannis Dendramis & Elias Tzavalis & Aikaterini Cheimarioti, 2025. "Measuring the Default Risk of Small Business Loans: Improved Credit Risk Prediction Using Deep Learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(7), pages 2277-2297, November.
  89. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
  90. Fausch, Jürg & Frigg, Moreno & Ruenzi, Stefan & Weigert, Florian, 2026. "Machine learning mutual fund flows," CFR Working Papers 26-03, University of Cologne, Centre for Financial Research (CFR).
  91. Pijush Kanti Das & Prabir Kumar Das, 2024. "Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 493-517, June.
  92. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
  93. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
  94. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  95. Pijush Kanti Das & Prabir Kumar Das, 2025. "Artificial Intelligence and Inflation Forecasting: A Contemporary Perspective," South Asian Journal of Macroeconomics and Public Finance, , vol. 14(1), pages 133-164, June.
  96. Wang, Zhenzhong & Zhu, Zhengyuan & Yu, Cindy, 2025. "Variable Selection in Macroeconomic Forecasting with Many Predictors," Econometrics and Statistics, Elsevier, vol. 36(C), pages 19-36.
  97. repec:osf:socarx:d8jkt_v1 is not listed on IDEAS
  98. Shovon Sengupta & Bhanu Pratap & Amit Pawar, 2025. "Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning," Papers 2504.05350, arXiv.org.
  99. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
  100. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024. "Lessons from nowcasting GDP across the world," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 8, pages 187-217, Edward Elgar Publishing.
  101. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
  102. Jorge M. Uribe & Oscar Valencia, 2024. "Taking the Pulse of Fiscal Distress: Inflation, Depreciation, and Crises," IREA Working Papers 202416, University of Barcelona, Research Institute of Applied Economics, revised Dec 2024.
  103. Klieber, Karin & Coulombe, Philippe Goulet, 2025. "Opening the black box of local projections," Working Paper Series 3105, European Central Bank.
  104. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
  105. repec:bge:wpaper:1245 is not listed on IDEAS
  106. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
  107. Jihad El Hokayem & Ibrahim Jamali & Ale Hejase, 2024. "A forecasting model for oil prices using a large set of economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1615-1624, August.
  108. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
  109. Sun, Weixin & Wang, Yong & Zhang, Li & Chen, Xihui Haviour & Hoang, Yen Hai, 2025. "Enhancing economic cycle forecasting based on interpretable machine learning and news narrative sentiment," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
  110. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
  111. Maximilian Göbel & Philippe Goulet Coulombe & Karin Klieber, 2025. "Dual Interpretation of Machine Learning Forecasts (Philippe Goulet Coulombe, Maximilian Göbel, Karin Klieber)," Working Papers 265, Oesterreichische Nationalbank (Austrian Central Bank).
  112. Adämmer, Philipp & Prüser, Jan & Schüssler, Rainer A., 2025. "Forecasting macroeconomic tail risk in real time: Do textual data add value?," International Journal of Forecasting, Elsevier, vol. 41(1), pages 307-320.
  113. Diana Barro & Antonella Basso & Marco Corazza & Guglielmo Alessandro Visentin, 2025. "A Neural Network-VAR for Long-Term Forecasting: An Application to Monetary Policy Effects in the Euro Area," Working Papers 2025: 24, Department of Economics, University of Venice "Ca' Foscari".
  114. Yucheng Yang & Yue Pang & Guanhua Huang & Weinan E, 2020. "The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data," Papers 2010.05172, arXiv.org.
  115. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
  116. Li, Haiqi & Zhang, Ni & Zhou, Jin, 2025. "A new self-normalized forecast comparison test," Economics Letters, Elsevier, vol. 256(C).
  117. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
  118. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
  119. Florian Huber & Massimiliano Marcellino & Tobias Scheckel, 2023. "Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification," Papers 2304.07856, arXiv.org, revised Sep 2025.
  120. Jozef Baruník & Luboš Hanus, 2025. "Taming Data‐Driven Probability Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 676-691, March.
  121. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
  122. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
  123. Moffo, Ahmadou Mustapha Fonton, 2024. "A machine learning approach in stress testing US bank holding companies," International Review of Financial Analysis, Elsevier, vol. 95(PC).
  124. Bachmair, K. & Schmitz, N., 2025. "Forecasting Macro with Finance," Cambridge Working Papers in Economics 2574, Faculty of Economics, University of Cambridge.
  125. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
  126. repec:hal:journl:hal-04675599 is not listed on IDEAS
  127. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
  128. Goulet Coulombe Philippe, 2025. "To Bag is to Prune," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 29(6), pages 669-697.
  129. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
  130. Julian Ashwin & Eleni Kalamara & Lorena Saiz, 2024. "Nowcasting Euro area GDP with news sentiment: A tale of two crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 887-905, August.
  131. Luca Attolico, 2025. "Explainable Machine Learning for Macroeconomic and Financial Nowcasting: A Decision-Grade Framework for Business and Policy," Papers 2512.00399, arXiv.org.
  132. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
  133. Zhang, Hongwei & Wang, Wentao & Niu, Zibo, 2024. "Geopolitical risks and crude oil futures volatility: Evidence from machine learning," Resources Policy, Elsevier, vol. 98(C).
  134. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
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