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Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

Citations

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Cited by:

  1. A. Frenkel’ A. & N. Volkova N. & A. Surkov A. & E. Romanyuk I. & А. Френкель А. & Н. Волкова Н. & А. Сурков А. & Э. Романюк И., 2018. "Использование Методов Гребневой Регрессии При Объединении Прогнозов // The Application Of Ridge Regression Methods When Combining Forecasts," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(4), pages 6-17.
  2. 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.
  3. Sergei V. Akopov, 2018. "“Duty” and “Blame” in Russian Official Symbolic Representations of Sovereignty (1994-2018)," HSE Working papers WP BRP 61/PS/2018, National Research University Higher School of Economics.
  4. Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Dec 2025.
  5. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
  6. Wei, Yu & Liang, Chao & Li, Yan & Zhang, Xunhui & Wei, Guiwu, 2020. "Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models," Finance Research Letters, Elsevier, vol. 35(C).
  7. Daiki Maki & Yasushi Ota, 2019. "Robust tests for ARCH in the presence of the misspecified conditional mean: A comparison of nonparametric approches," Papers 1907.12752, arXiv.org, revised Sep 2019.
  8. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
  9. Chukwuemeka Eneh & Gurjit S. Randhawa & Masoud Karbasi & Aitazaz Ahsan Farooque & Mumtaz Ali, 2026. "Improving multi-step drought forecasting in Atlantic Canada through variational mode decomposition and machine learning: The role of sand-cat swarm optimization technique in kernel ridge regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(7), pages 1-35, April.
  10. Peter Exterkate, 2011. "Modelling Issues in Kernel Ridge Regression," Tinbergen Institute Discussion Papers 11-138/4, Tinbergen Institute.
  11. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Risks, MDPI, vol. 6(1), pages 1-20, March.
  12. Milan Fičura, 2019. "Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks," FFA Working Papers 1.001, Prague University of Economics and Business, revised 24 Nov 2019.
  13. Seeun Park & Hee‐Seok Oh & Yaeji Lim, 2025. "Combined Quantile Forecasting for High‐Dimensional Non‐Gaussian Data," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
  14. Alessandro Giovannelli, 2012. "Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies," CEIS Research Paper 255, Tor Vergata University, CEIS, revised 08 Nov 2012.
  15. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
  16. Peter Exterkate, 2012. "Model Selection in Kernel Ridge Regression," CREATES Research Papers 2012-10, Department of Economics and Business Economics, Aarhus University.
  17. 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.
  18. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
  19. Wojciech Victor Fulmyk, 2023. "Nonlinear Granger Causality using Kernel Ridge Regression," Papers 2309.05107, arXiv.org.
  20. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
  21. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
  22. Oslandsbotn, Andreas & Kereta, Željko & Naumova, Valeriya & Freund, Yoav & Cloninger, Alexander, 2022. "StreaMRAK a streaming multi-resolution adaptive kernel algorithm," Applied Mathematics and Computation, Elsevier, vol. 426(C).
  23. Abolghasemi, Mahdi & Abbasi, Babak & HosseiniFard, Zahra, 2025. "Machine learning for satisficing operational decision making: A case study in blood supply chain," International Journal of Forecasting, Elsevier, vol. 41(1), pages 3-19.
  24. Tian Han & Ying Wang & Xiao Wang & Kang Chen & Huaiwu Peng & Zhenxin Gao & Lanxin Cui & Wentong Sun & Qinke Peng, 2023. "Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
  25. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
  26. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.
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