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Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

Citations

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Cited by:

  1. Naijing Huang & Yuqing Qi & Jie Xia, 2025. "China’s inflation forecasting in a data-rich environment: based on machine learning algorithms," Applied Economics, Taylor & Francis Journals, vol. 57(17), pages 1995-2020, April.
  2. Rehim Kılıç, 2025. "Linear and nonlinear econometric models against machine learning models: realized volatility prediction," Finance and Economics Discussion Series 2025-061, Board of Governors of the Federal Reserve System (U.S.).
  3. Peiwen Zhang & Yunan Luo & Qian Yu & Zhifeng Zhou, 2025. "Air transportation carbon dioxide emission forecasting: An improved back propagation neural network," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-26, October.
  4. Nason, Guy P. & Palasciano, Henry Antonio, 2026. "Forecasting UK consumer price inflation with RaGNAR: Random generalised network autoregressive processes," International Journal of Forecasting, Elsevier, vol. 42(1), pages 181-202.
  5. Patricia Toledo & Roberto Duncan, 2024. "Forecasting food price inflation during global crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1087-1113, July.
  6. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
  7. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
  8. Marcus Araujo & Francisco Rodrigues & Elaine Sousa, 2026. "EconoGNN: A graph neural network framework for temporal economic resilience insights," PLOS ONE, Public Library of Science, vol. 21(4), pages 1-24, April.
  9. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
  10. 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.
  11. Luca Bacco & Tiziana Laureti & Juri Marcucci & Luigi Palumbo & Daniele Sasso & Luca Vollero, 2026. "Nowcasting the Italian consumer price index using online prices and machine learning," Questioni di Economia e Finanza (Occasional Papers) 1026, Bank of Italy, Economic Research and International Relations Area.
  12. Kaiji Chen & Mr. Yunhui Zhao, 2024. "Chinese Housing Market Sentiment Index: A Generative AI Approach and An Application to Monetary Policy Transmission," IMF Working Papers 2024/264, International Monetary Fund.
  13. Soňa Benecká, 2026. "Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(5), pages 2458-2501, August.
  14. Sengupta, Shovon & Chakraborty, Tanujit & Singh, Sunny Kumar, 2025. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," International Journal of Forecasting, Elsevier, vol. 41(3), pages 953-981.
  15. Rodion Latypov & Elena Akhmedova & Egor Postolit & Marina Mikitchuk, 2024. "Bottom-up Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 23-44, September.
  16. Fang, Yi & Chen, Yuzhi & Ren, Hang, 2023. "A factor pricing model based on machine learning algorithm," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 280-297.
  17. Krystian Jaworski, 2026. "AI‐Driven Inflation Forecasting in the Aftermath of COVID‐19," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(5), pages 2525-2548, August.
  18. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
  19. Christian Beer & Robert Ferstl & Bernhard Graf, 2025. "Improving disaggregated short-term food inflation forecasts with webscraped data (Christian Beer, Robert Ferstl, Bernhard Graf)," Working Papers 262, Oesterreichische Nationalbank (Austrian Central Bank).
  20. Maya Vilenko, 2025. "BiHRNN -- Bi-Directional Hierarchical Recurrent Neural Network for Inflation Forecasting," Papers 2503.01893, arXiv.org.
  21. Hasan ŞENGÜLER & Berat KARA, 2025. "Forecasting the Inflation for Budget Forecasters: An Analysis of ANN Model Performance in Türkiye," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 10(1), pages 58-91.
  22. Jonathan Leslie, 2023. "Seeing the Future: Improving Macroeconomic Forecasts with Spatial Data and Recurrent Convolutional Neural Networks," CAEPR Working Papers 2023-003 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
  23. Xiangjuan Liu & Yunlong Li & Fengtong Wang & Yujie Qin & Zhongyu Lyu, 2025. "Decomposition-reconstruction-optimization framework for hog price forecasting: Integrating STL, PCA, and BWO-optimized BiLSTM," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-29, June.
  24. Castello, Oleksandr & Resta, Marina, 2025. "Univariate and multivariate forecasting of the electricity futures curve using Dynamic Recurrent Neural Networks," Applied Energy, Elsevier, vol. 394(C).
  25. Qian, Yu & Xu, Zeshui & Qin, Yong & Gou, Xunjie & Skare, Marinko, 2026. "Fiscal policy responses to the EU energy crisis: Impact on inflation and energy prices," Journal of Policy Modeling, Elsevier, vol. 48(3).
  26. 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.
  27. Masahiro Suzuki & Hiroki Sakaji, 2024. "Refined and Segmented Price Sentiment Indices from Survey Comments," Papers 2411.09937, arXiv.org, revised Nov 2024.
  28. Yue Li & Shujuan Chen & Ying Jin, 2026. "Mukara: A deep learning alternative to the four-step travel demand model with a case study on interurban highway traffic prediction in the UK," PLOS ONE, Public Library of Science, vol. 21(4), pages 1-28, April.
  29. Juan Tenorio & Heidi Alpiste & Jakelin Rem'on & Arian Segil, 2025. "An Artificial Trend Index for Private Consumption Using Google Trends," Papers 2503.21981, arXiv.org.
  30. Suleiman O. Mamman, 2026. "Forecasting inflation in a warming world: regional heterogeneity and machine learning evidence from Asia," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 75(1), pages 1-33, March.
  31. Phaik Nie Chin & Abdulsalam Abuhamra & Zheng Xian Lee, 2024. "The Determinants of Malaysian Real Estate Investment Trusts’ Systematic Risks," Capital Markets Review, Malaysian Finance Association, vol. 32(2), pages 1-26.
  32. Injae Seo & Minkyoung Kim & Jong Wook Kim & Beakcheol Jang, 2025. "Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-28, May.
  33. Sona Benecka, 2025. "Forecasting Disaggregated Producer Prices: A Fusion of Machine Learning and Econometric Techniques," Working Papers 2025/2, Czech National Bank, Research and Statistics Department.
  34. 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.
  35. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
  36. Beer, Christian & Ferstl, Robert & Graf, Bernhard, 2026. "Improving disaggregated short-term food inflation forecasts with webscraped data," International Journal of Forecasting, Elsevier, vol. 42(3), pages 1047-1068.
  37. Oleg Semiturkin & Andrey Shevelev, 2023. "Correct Comparison of Predictive Features of Machine Learning Models: The Case of Forecasting Inflation Rates in Siberia," Russian Journal of Money and Finance, Bank of Russia, vol. 82(1), pages 87-103, March.
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