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How is Machine Learning Useful for Macroeconomic Forecasting?

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Cited by:

  1. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
  2. Rincón Briceño, Juan José, 2025. "Colombian economic activity nowcasting: addressing nonlinearities and high dimensionality through machine-learning," Documentos CEDE 21388, Universidad de los Andes, Facultad de Economía, CEDE.
  3. 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.
  4. 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.
  5. 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).
  6. 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).
  7. 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.
  8. 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.
  9. 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.
  10. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.
  11. 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.
  12. 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.
  13. 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.
  14. 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).
  15. 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.
  16. 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.
  17. Zhang, Hongwei & Wang, Wentao & Niu, Zibo, 2024. "Geopolitical risks and crude oil futures volatility: Evidence from machine learning," Resources Policy, Elsevier, vol. 98(C).
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
  24. Yucheng Yang & Yue Pang & Guanhua Huang & Weinan E, 2020. "The Knowledge Graph for Macroeconomic Analysis with Alternative Big Data," Papers 2010.05172, arXiv.org.
  25. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
  26. 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.
  27. 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.
  28. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
  29. 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.
  30. repec:hal:journl:hal-04675599 is not listed on IDEAS
  31. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
  32. 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.
  33. 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.
  34. Shovon Sengupta & Bhanu Pratap & Amit Pawar, 2025. "Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning," Papers 2504.05350, arXiv.org.
  35. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  36. 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).
  37. 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.
  38. 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.
  39. 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.
  40. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
  41. 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.
  42. 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.
  43. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
  44. 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).
  45. Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Feb 2025.
  46. 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.
  47. 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.
  48. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
  49. 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).
  50. 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.
  51. 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).
  52. 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).
  53. 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.
  54. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
  55. Sihan Tu & Zhaoxing Gao, 2025. "A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting," Papers 2502.15275, arXiv.org.
  56. Clément Cariou & Amélie Charles & Olivier Darné, 2024. "Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2341-2357, September.
  57. Florian Huber & Massimiliano Marcellino, 2023. "Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification," Papers 2304.07856, arXiv.org, revised May 2023.
  58. 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.
  59. 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.
  60. Jozef Baruník & Luboš Hanus, 2025. "Taming Data‐Driven Probability Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 676-691, March.
  61. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
  62. 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.
  63. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
  64. 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.
  65. 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 (IfW Kiel).
  66. Mihail Yanchev, 2025. "Interval, Quantile and Density Forecasts," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 109-129, March.
  67. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
  68. 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.
  69. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
  70. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
  71. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
  77. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
  78. 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).
  79. Midha, Joshua, 2024. "Assessing Emerging Markets through Transactional Dynamics: A New Multi-Dimensional Valuation Framework," SocArXiv d8jkt, Center for Open Science.
  80. 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.
  81. 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.
  82. 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.
  83. 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.
  84. Iva Glišic, 2024. "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin 22, National Bank of Serbia.
  85. Philippe Goulet Coulombe & Karin Klieber, 2025. "Opening the Black Box of Local Projections," Papers 2505.12422, arXiv.org.
  86. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
  87. 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).
  88. 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.
  89. Pietro Bogani & Matteo Fontana & Luca Neri & Simone Vantini, 2024. "Calibrated quantile prediction for Growth-at-Risk," Papers 2411.00520, arXiv.org.
  90. Ajit Desai, 2023. "Machine learning for economics research: when, what and how," Staff Analytical Notes 2023-16, Bank of Canada.
  91. 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.
  92. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
  93. 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).
  94. 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.
  95. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
  96. 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.
  97. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
  98. 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.
  99. 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.
  100. Nicolas Gavoille, 2025. "A short drop or a sudden stop? Sanctions, trade shocks, and firms' adjustment margins," Working Papers 2025/03, Latvijas Banka.
  101. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
  102. 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.
  103. Alain Dudoit & Molivann Panot & Thierry Warin, 2021. "Towards a multi-stakeholder Intermodal Trade-Transportation Data-Sharing and Knowledge Exchange Network," CIRANO Project Reports 2021rp-28, CIRANO.
  104. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
  105. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
  106. 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.
  107. 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.
  108. repec:osf:socarx:d8jkt_v1 is not listed on IDEAS
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