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Macroeconomic Indicator Forecasting with Deep Neural Networks

Citations

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Cited by:

  1. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers 14/2019, Bank of Finland.
  2. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
  3. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
  4. Tänzer, Alina, 2024. "The effectiveness of central bank purchases of long-term treasury securities: A neural network approach," IMFS Working Paper Series 204, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
  5. 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.
  6. 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.
  7. Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079, arXiv.org.
  8. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
  9. Zhu, Felix & Dong, Yumo & Huang, Fei, 2025. "Data-rich economic forecasting for actuarial applications," Insurance: Mathematics and Economics, Elsevier, vol. 124(C).
  10. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
  11. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
  12. Alexey Bilgaev & Suocheng Dong & Fujia Li & Hao Cheng & Arnold Tulohonov & Erzhena Sadykova & Anna Mikheeva, 2020. "Baikal Region (Russia) Development Prospects Based on the Green Economy Principles," Sustainability, MDPI, vol. 13(1), pages 1-22, December.
  13. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
  14. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
  15. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
  16. 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.
  17. Matthew Smith & Francisco Alvarez, 2025. "Machine Learning for Applied Economic Analysis: Gaining Practical Insights," Working Papers 2025-03, FEDEA.
  18. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
  19. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
  20. 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.
  21. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
  22. Tänzer, Alina, 2024. "Multivariate macroeconomic forecasting: From DSGE and BVAR to artificial neural networks," IMFS Working Paper Series 205, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
  23. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
  24. 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).
  25. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
  26. Cameron Fen & Samir Undavia, 2022. "Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model," Papers 2203.06540, arXiv.org.
  27. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
  28. 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.
  29. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
  30. Faridoon Khan & Hasnain Iftikhar & Imran Khan & Paulo Canas Rodrigues & Abdulmajeed Atiah Alharbi & Jeza Allohibi, 2025. "A Hybrid Vector Autoregressive Model for Accurate Macroeconomic Forecasting: An Application to the U.S. Economy," Mathematics, MDPI, vol. 13(11), pages 1-16, May.
  31. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
  32. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.
  33. Anna Almosova & Niek Andresen, 2023. "Nonlinear inflation forecasting with recurrent neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 240-259, March.
  34. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
  35. Anh Le Hoang & Hao Nguyen Van & Bao Ho Nguyen Thai & Thinh Vo Huynh Hung & Van Nguyen Truc, 2025. "Application Of Artificial Neural Network Model To Forecast Growth Rate Of Service Sector’s Value In Ho Chi Minh City," WSB Journal of Business and Finance, Sciendo, vol. 59(1), pages 60-70.
  36. Hongyi Liu, 2025. "Deep Learning for Conditional Asset Pricing Models," Papers 2509.04812, arXiv.org.
  37. 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.
  38. Pedro Gerber Machado & Julia Tomei & Adam Hawkes & Celma de Oliveira Ribeiro, 2020. "A Simulator to Determine the Evolution of Disparities in Food Consumption between Socio-Economic Groups: A Brazilian Case Study," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
  39. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
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