Machine learning at central banks
Author
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
- Isaac Mbiti & David N. Weil, 2015.
"Mobile Banking: The Impact of M-Pesa in Kenya,"
NBER Chapters, in: African Successes, Volume III: Modernization and Development, pages 247-293,
National Bureau of Economic Research, Inc.
- Isaac Mbiti & David N. Weil, 2011. "Mobile Banking: The Impact of M-Pesa in Kenya," Working Papers 2011-13, Brown University, Department of Economics.
- Isaac Mbiti & David N. Weil, 2011. "Mobile Banking: The Impact of M-Pesa in Kenya," NBER Working Papers 17129, National Bureau of Economic Research, Inc.
- Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
- Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
- Aikman, David & Galesic, Mirta & Gigerenzer, Gerd & Kapadia, Sujit & Katsikopoulos, Konstantinos & Kothiyal, Amit & Murphy, Emma & Neumann, Tobias, 2014. "Financial Stability Paper No 28: Taking uncertainty seriously - simplicity versus complexity in financial regulation," Bank of England Financial Stability Papers 28, Bank of England.
- Philippe Bracke & Silvana Tenreyro, 2021.
"History Dependence in the Housing Market,"
American Economic Journal: Macroeconomics, American Economic Association, vol. 13(2), pages 420-443, April.
- Bracke, Philippe & Tenreyro, Silvana, 2016. "History dependence in the housing market," Bank of England working papers 630, Bank of England, revised 13 Jul 2018.
- Bracke, Philippe & Tenreyro, Silvana, 2018. "History dependence in the housing market," LSE Research Online Documents on Economics 91690, London School of Economics and Political Science, LSE Library.
- Silvana Tenreyro & Philippe Bracke, 2017. "History Dependence in the Housing Market," 2017 Meeting Papers 423, Society for Economic Dynamics.
- Philippe Bracke & Silvana Tenreyro, 2016. "History Dependence in the Housing Market," Discussion Papers 1635, Centre for Macroeconomics (CFM).
- Bracke, Philippe & Tenreyro, Silvana, 2016. "History dependence in the housing market," LSE Research Online Documents on Economics 86176, London School of Economics and Political Science, LSE Library.
- Philippe Bracke & Silvana Tenreyro, 2018. "History dependence in the housing market," CEP Discussion Papers dp1568, Centre for Economic Performance, LSE.
- Bracke, Philippe & Tenreyro, Silvana, 2021. "History dependence in the housing market," LSE Research Online Documents on Economics 103079, London School of Economics and Political Science, LSE Library.
- Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002.
"strucchange: An R Package for Testing for Structural Change in Linear Regression Models,"
Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
- Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2001. "Strucchange: An R package for testing for structural change in linear regression models," Technical Reports 2001,26, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Jihad Dagher & Giovanni Dell'Ariccia & Luc Laeven & Lev Ratnovski & Hui Tong, 2016. "Benefits and Costs of Bank Capital," IMF Staff Discussion Notes 16/04, International Monetary Fund.
- Atz, Ulrich & Bholat, David, 2016. "Peer-to-peer lending and financial innovation in the United Kingdom - Ulrich Atz and David Bholat," Bank of England working papers 598, Bank of England.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- David Aikman & Mirta Galesic & Gerd Gigerenzer & Sujit Kapadia & Konstantinos Katsikopoulos & Amit Kothiyal & Emma Murphy & Tobias Neumann, 2021.
"Taking uncertainty seriously: simplicity versus complexity in financial regulation [Uncertainty in macroeconomic policy-making: art or science?],"
Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(2), pages 317-345.
- Aikman, David & Galesic, Mirta & Gigerenzer, Gerd & Kapadia, Sujit & Katsikopolous, Konstantinos & Kothiyal, Amit & Murphy, Emma & Neumann, Tobias, 2014. "Taking Uncertainty Seriously: Simplicity versus Complexity in Financial Regulation," MPRA Paper 59908, University Library of Munich, Germany.
- Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
- Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
- Brooke, Martin & Bush, Oliver & Edwards, Robert & Ellis, Jas & Francis, Bill & Harimohan, Rashmi & Neiss, Katharine & Siegert, Caspar, 2015. "Financial Stability Paper No. 35: Measuring the macroeconomic costs and benefits of higher UK bank capital requirements -," Bank of England Financial Stability Papers 35, Bank of England.
- Vaart Aad W. van der & Dudoit Sandrine & Laan Mark J. van der, 2006. "Oracle inequalities for multi-fold cross validation," Statistics & Risk Modeling, De Gruyter, vol. 24(3), pages 351-371, December.
- Alberto Abadie & Kasy, Maximilian, 2017. "The risk of machine learning," Working Paper 383316, Harvard University OpenScholar.
- Harding, Matthew & Lovenheim, Michael, 2017.
"The effect of prices on nutrition: Comparing the impact of product- and nutrient-specific taxes,"
Journal of Health Economics, Elsevier, vol. 53(C), pages 53-71.
- Matthew Harding & Michael Lovenheim, 2014. "The Effect of Prices on Nutrition: Comparing the Impact of Product- and Nutrient-Specific Taxes," NBER Working Papers 19781, National Bureau of Economic Research, Inc.
- Matthew Harding & Michael Lovenheim, 2014. "The Effect of Prices on Nutrition: Comparing the Impact of Product- and Nutrient-Specific Taxes," Discussion Papers 13-023, Stanford Institute for Economic Policy Research.
- David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
- Olga Cielinska & Andreas Joseph & Ujwal Shreyas & John Tanner & Michalis Vasios, 2017.
"Gauging market dynamics using trade repository data: The case of the Swiss franc de-pegging,"
IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43,
Bank for International Settlements.
- Cielinska, Olga & Joseph, Andreas & Shreyas, Ujwal & Tanner, John & Vasios, Michalis, 2017. "Gauging market dynamics using trade repository data: the case of the Swiss franc de-pegging," Bank of England Financial Stability Papers 41, Bank of England.
- Matthew Harding & Carlos Lamarche, 2016. "Empowering Consumers Through Data and Smart Technology: Experimental Evidence on the Consequences of Time‐of‐Use Electricity Pricing Policies," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(4), pages 906-931, September.
- Lacher, R. C. & Coats, Pamela K. & Sharma, Shanker C. & Fant, L. Franklin, 1995. "A neural network for classifying the financial health of a firm," European Journal of Operational Research, Elsevier, vol. 85(1), pages 53-65, August.
- Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
- Justin Sirignano & Apaar Sadhwani & Kay Giesecke, 2016. "Deep Learning for Mortgage Risk," Papers 1607.02470, arXiv.org, revised Mar 2018.
- Jihad Dagher & Mr. Giovanni Dell'Ariccia & Mr. Luc Laeven & Mr. Lev Ratnovski & Mr. Hui Tong, 2016. "Benefits and Costs of Bank Capital," IMF Staff Discussion Notes 2016/004, International Monetary Fund.
- Daas Piet J.H. & Puts Marco J. & Buelens Bart & Hurk Paul A.M. van den, 2015. "Big Data as a Source for Official Statistics," Journal of Official Statistics, Sciendo, vol. 31(2), pages 249-262, June.
- Susan Athey & Guido Imbens, 2015. "Recursive Partitioning for Heterogeneous Causal Effects," Papers 1504.01132, arXiv.org, revised Dec 2015.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
- Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
- Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.
- Alan Kirman, 2016. "Complexity and Economic Policy: A Paradigm Shift or a Change in Perspective? A Review Essay on David Colander and Roland Kupers's Complexity and the Art of Public Policy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 534-572, June.
- Li, Q. & Wang, Suojin, 1998. "A simple consistent bootstrap test for a parametric regression function," Journal of Econometrics, Elsevier, vol. 87(1), pages 145-165, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Aikman, David & Haldane, Andrew & Hinterschweiger, Marc & Kapadia, Sujit, 2018. "Rethinking financial stability," Bank of England working papers 712, Bank of England.
- Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
- Leif Anders Thorsrud, 2016.
"Nowcasting using news topics Big Data versus big bank,"
Working Papers
No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
- Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
- Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Économétrie & Machine Learning," Working Papers hal-01568851, HAL.
- Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
- Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
- Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
- Dominika Ehrenbergerová & Martin Hodula & Zuzana Gric, 2022.
"Does capital-based regulation affect bank pricing policy?,"
Journal of Regulatory Economics, Springer, vol. 61(2), pages 135-167, April.
- Dominika Ehrenbergerova & Martin Hodula & Zuzana Rakovska, 2020. "Does Capital-Based Regulation Affect Bank Pricing Policy?," Working Papers 2020/5, Czech National Bank.
- Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
- Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
- Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
- du Jardin, Philippe & Séverin, Eric, 2011.
"Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model,"
MPRA Paper
44262, University Library of Munich, Germany.
- P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
- Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
- Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022.
"How effective is carbon pricing?—A machine learning approach to policy evaluation,"
Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
- Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2021. "How effective is carbon pricing? A machine learning approach to policy evaluation," ZEW Discussion Papers 21-039, ZEW - Leibniz Centre for European Economic Research.
- de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
- Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020.
"News Media vs. FRED-MD for Macroeconomic Forecasting,"
CESifo Working Paper Series
8639, CESifo.
- Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Paper 2020/14, Norges Bank.
- Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
- Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
More about this item
Keywords
Machine learning; artificial intelligence; big data; econometrics; forecasting; inflation; financial markets; banking supervision; financial technology;All these keywords.
JEL classification:
- A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
- A33 - General Economics and Teaching - - Multisubject Collective Works - - - Handbooks
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- Y20 - Miscellaneous Categories - - Introductions and Prefaces - - - Introductions and Prefaces
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2017-09-10 (Big Data)
- NEP-CBA-2017-09-10 (Central Banking)
- NEP-CMP-2017-09-10 (Computational Economics)
- NEP-ECM-2017-09-10 (Econometrics)
- NEP-MAC-2017-09-10 (Macroeconomics)
- NEP-MON-2017-09-10 (Monetary Economics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boe:boeewp:0674. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Digital Media Team (email available below). General contact details of provider: https://edirc.repec.org/data/boegvuk.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.