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Machine learning at central banks

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:

  1. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
  2. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
  3. Carlos Moreno Pérez & Marco Minozzo, 2022. "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers 2240, Banco de España.
  4. repec:zbw:bofitp:2019_008 is not listed on IDEAS
  5. Amarda Cano, 2021. "Evolution of Public Debt in Albania during 1990-2017 and its impact on the Economic Growth," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, ejme_v4_i.
  6. Rohan Arora & Chen Fan & Guillaume Ouellet Leblanc, 2019. "Liquidity Management of Canadian Corporate Bond Mutual Funds: A Machine Learning Approach," Staff Analytical Notes 2019-7, Bank of Canada.
  7. Vittoria La Serra & Emiliano Svezia, 2024. "A supervised record linkage approach for anomaly detection in insurance assets granular data," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4181-4205, October.
  8. Emanuel Kohlscheen, 2022. "Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices," Papers 2208.14254, arXiv.org, revised Oct 2022.
  9. 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.
  10. Amadxarif, Zahid & Brookes, James & Garbarino, Nicola & Patel, Rajan & Walczak, Eryk, 2019. "The language of rules: textual complexity in banking reforms," Bank of England working papers 834, Bank of England.
  11. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
  12. Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
  13. Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
  14. Defina, Ryan, 2021. "Machine Learning Methods: Potential for Deposit Insurance," MPRA Paper 110712, University Library of Munich, Germany.
  15. Emanuel Kohlscheen, 2021. "What does machine learning say about the drivers of inflation?," BIS Working Papers 980, Bank for International Settlements.
  16. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
  17. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  18. Andreas Joseph, 2019. "From interpretability to inference: an estimation framework for universal approximators," Papers 1903.04209, arXiv.org, revised Dec 2024.
  19. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
  20. Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  21. Long Ren & Shaojie Cong & Xinlong Xue & Daqing Gong, 2024. "Credit rating prediction with supply chain information: a machine learning perspective," Annals of Operations Research, Springer, vol. 342(1), pages 657-686, November.
  22. 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.
  23. Joseph, Andreas & Vasios, Michalis, 2022. "OTC Microstructure in a period of stress: A Multi-layered network approach," Journal of Banking & Finance, Elsevier, vol. 138(C).
  24. 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.
  25. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  26. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
  27. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
  28. Guerra, Pedro & Castelli, Mauro & Côrte-Real, Nadine, 2022. "Machine learning for liquidity risk modelling: A supervisory perspective," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 175-187.
  29. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
  30. Romain Plassard, 2020. "Making a Breach: The Incorporation of Agent-Based Models into the Bank of England's Toolkit," GREDEG Working Papers 2020-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
  31. Funke, Michael & Tsang, Andrew, 2019. "The direction and intensity of China's monetary policy conduct: A dynamic factor modelling approach," BOFIT Discussion Papers 8/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
  32. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," WiSo-HH Working Paper Series 62, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
  33. Leonard Sabetti & Ronald Heijmans, 2020. "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers 681, DNB.
  34. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
  35. 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.
  36. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
  37. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2021. "Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting," Questioni di Economia e Finanza (Occasional Papers) 611, Bank of Italy, Economic Research and International Relations Area.
  38. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
  39. Funke, Michael & Tsang, Andrew, 2019. "The direction and intensity of China's monetary policy conduct: A dynamic factor modelling approach," BOFIT Discussion Papers 8/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
  40. Tamara, Novian & Dwi Muchisha, Nadya & Andriansyah, Andriansyah & Soleh, Agus M, 2020. "Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms," MPRA Paper 105235, University Library of Munich, Germany.
  41. Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
  42. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
  43. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
  44. Bholat, David & Brookes, James & Cai, Chris & Grundy, Katy & Lund, Jakob, 2017. "Sending firm messages: text mining letters from PRA supervisors to banks and building societies they regulate," Bank of England working papers 688, Bank of England.
  45. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
  46. Andrea Carboni & Alessandro Moro, 2018. "Imputation techniques for the nationality of foreign shareholders in Italian firms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), External sector statistics: current issues and new challenges, volume 48, Bank for International Settlements.
  47. Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
  48. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
  49. 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.
  50. Natalia Nehrebecka, 2021. "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 719-736.
  51. Andrei Shevelev & Maria Kvaktun & Kristina Virovets, 2021. "Effect of Monetary Policy on Investment in Russian Regions," Russian Journal of Money and Finance, Bank of Russia, vol. 80(4), pages 31-49, December.
  52. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
  53. Andrew Clark, 2020. "A Pound Centric look at the Pound vs. Krona Exchange Rate Movement from 1844 to 1965," Economics Discussion Papers em-dp2020-22, Department of Economics, University of Reading.
  54. Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
  55. Moreno-Pérez, Carlos & Minozzo, Marco, 2024. "‘Making text talk’: The minutes of the Central Bank of Brazil and the real economy," Journal of International Money and Finance, Elsevier, vol. 147(C).
  56. Bogner Alexandra & Jerger Jürgen, 2023. "Big data in monetary policy analysis—a critical assessment," Economics and Business Review, Sciendo, vol. 9(2), pages 27-40, April.
  57. Fabio Zambuto, 2021. "Quality checks on granular banking data: an experimental approach based on machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Micro data for the macro world, volume 53, Bank for International Settlements.
  58. Fabio Zambuto & Simona Arcuti & Roberto Sabatini & Daniele Zambuto, 2021. "Application of classification algorithms for the assessment of confirmation to quality remarks," Questioni di Economia e Finanza (Occasional Papers) 631, Bank of Italy, Economic Research and International Relations Area.
  59. Joe McLaughlin & Nathan Palmer & Adam Minson & Eric Parolin, 2018. "The OFR Financial System Vulnerabilities Monitor," Working Papers 18-01, Office of Financial Research, US Department of the Treasury.
  60. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
  61. Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
  62. Joseph, Andreas & Vasios, Michalis & Maizels, Olga & Shreyas, Ujwal & Tanner, John, 2019. "OTC microstructure in a period of stress: a multi‑layered network approach," Bank of England working papers 832, Bank of England.
  63. 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.
  64. Andrew Tsang, 2024. "Uncovering heterogeneous regional impacts of Chinese monetary policy," Empirical Economics, Springer, vol. 67(3), pages 915-940, September.
  65. Sabetti, Leonard & Heijmans, Ronald, 2021. "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
  66. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the curse of dimensionality: quantitative economics with deep learning," Working Papers 2444, Banco de España.
  67. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
  68. Vadim Grishchenko & Ivan Krylov, 2024. "New Approaches to Measuring, Analysing, and Forecasting Prices: A Review of the Bank of Russia, NES, and HSE University Workshop," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 92-111, June.
  69. 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.
  70. Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
  71. Dmytro Krukovets, 2020. "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24.
  72. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
  73. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.
  74. Anil Savio Kavuri & Alistair Milne, 2019. "FinTech and the future of financial services: What are the research gaps?," CAMA Working Papers 2019-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  75. Lisa-Cheree Martin, 2019. "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers 12/2019, Stellenbosch University, Department of Economics.
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