Improving data quality and closing data gaps with machine learning
In: Data needs and Statistics compilation for macroprudential analysis
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- 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.
- Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Tomz, Michael & King, Gary & Zeng, Langche, 2003. "ReLogit: Rare Events Logistic Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i02).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Davide Nicola Continanza & Andrea del Monaco & Marco di Lucido & Daniele Figoli & Pasquale Maddaloni & Filippo Quarta & Giuseppe Turturiello, 2023.
"Stacking machine learning models for anomaly detection: comparing AnaCredit to other banking data sets,"
IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59,
Bank for International Settlements.
- Pasquale Maddaloni & Davide Nicola Continanza & Andrea del Monaco & Daniele Figoli & Marco di Lucido & Filippo Quarta & Giuseppe Turturiello, 2022. "Stacking machine-learning models for anomaly detection: comparing AnaCredit to other banking datasets," Questioni di Economia e Finanza (Occasional Papers) 689, Bank of Italy, Economic Research and International Relations Area.
- Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- 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.
- Fabio Zambuto & Maria Rosaria Buzzi & Giuseppe Costanzo & Marco Di Lucido & Barbara La Ganga & Pasquale Maddaloni & Fabio Papale & Emiliano Svezia, 2020. "Quality checks on granular banking data: an experimental approach based on machine learning?," Questioni di Economia e Finanza (Occasional Papers) 547, Bank of Italy, Economic Research and International Relations Area.
- 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.
- José María Serena Garralda & Bruno Tissot, 2018. "Central banks and trade repositories derivatives data," IFC Reports 7, Bank for International Settlements.
- Okiriza Wibisono & Hidayah Dhini Ari & Anggraini Widjanarti & Alvin Andhika Zulen & Bruno Tissot, 2019. "The use of big data analytics and artificial intelligence in central banking – An overview," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
- 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.
- Kh. Jitenkumar Singh & A. Jiran Meitei & Nongzaimayum Tawfeeq Alee & Mosoniro Kriina & Nirendrakumar Singh Haobijam, 2022. "Machine learning algorithms for predicting smokeless tobacco status among women in Northeastern States, India," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2629-2639, October.
- 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.
- Ezgi Deryol & Duygu Konukçu & Robert Szemere & Bruno Tissot, 2019. "Mind the data gap: commercial property prices for policy," IFC Reports 8, Bank for International Settlements.
- Claudia Biancotti & Alfonso Rosolia & Giovanni Veronese & Robert Kirchner & Francois Mouriaux, 2021. "Covid-19 and official statistics: a wakeup call?," Questioni di Economia e Finanza (Occasional Papers) 605, Bank of Italy, Economic Research and International Relations Area.
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.- 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.
- Gavoille, Nicolas & Zasova, Anna, 2023.
"What we pay in the shadows: Labor tax evasion, minimum wage hike and employment,"
Journal of Public Economics, Elsevier, vol. 228(C).
- Nicolas Gavoille & Anna Zasova, 2021. "What we pay in the shadow: Labor tax evasion, minimum wage hike and employment," Working Papers CEB 21-017, ULB -- Universite Libre de Bruxelles.
- Nicolas Gavoille & Anna Zasova, 2021. "What we pay in the shadows: Labor tax evasion, minimum wage hike and employment," SSE Riga/BICEPS Research Papers 6, Baltic International Centre for Economic Policy Studies (BICEPS);Stockholm School of Economics in Riga (SSE Riga).
- McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
- Max Nathan & Anna Rosso, 2017.
"Innovative events,"
Development Working Papers
429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
- Max Nathan & Anna Rosso, 2019. "Innovative events," CEP Discussion Papers dp1607, Centre for Economic Performance, LSE.
- Nathan, Max & Rosso, Anna, 2019. "Innovative Events," IZA Discussion Papers 12213, Institute of Labor Economics (IZA).
- Nathan, Max & Rosso, Anna, 2019. "Innovative events," LSE Research Online Documents on Economics 102626, London School of Economics and Political Science, LSE Library.
- Nathan, Max & Rosso, Anna, 2019. "Innovative Events," SocArXiv t3jrq, Center for Open Science.
- Martin-Shields, Charles P. & Stojetz, Wolfgang, 2019.
"Food security and conflict: Empirical challenges and future opportunities for research and policy making on food security and conflict,"
World Development, Elsevier, vol. 119(C), pages 150-164.
- Martin-Shields, Charles & Stojetz, Wolfgang, 2018. "Food security and conflict: Empirical challenges and future opportunities for research and policy making on food security and conflict," ESA Working Papers 288954, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," ERSA Working Paper Series, Economic Research Southern Africa, vol. 0.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
- Gordeev, Stepan & Steinbach, Sandro, 2024. "Determinants of PTA design: Insights from machine learning," International Economics, Elsevier, vol. 178(C).
- Gusarov, N. & Talebijmalabad, A. & Joly, I., 2020.
"Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness,"
Working Papers
2020-12, Grenoble Applied Economics Laboratory (GAEL).
- Nikita Gusarov & Amirreza Talebijamalabad & Iragaël Joly, 2020. "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers hal-03019739, HAL.
- Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
- Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
- 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.
- Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
- Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023.
"Housing, imputed rent, and household welfare,"
The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
- Ceriani,Lidia & Olivieri,Sergio Daniel & Ranzani,Marco, 2019. "Housing, Imputed Rent, and Households'Welfare," Policy Research Working Paper Series 8955, The World Bank.
- Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020.
"Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform,"
Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
- Christophe Croux & Julapa Jagtiani & Tarunsai Korivi & Milos Vulanovic, 2020. "Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform," Working Papers 20-15, Federal Reserve Bank of Philadelphia.
- David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021.
"Microstructure in the Machine Age [The risk of machine learning],"
The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
- David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang, 2021. "Microstructure in the Machine Age," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3316-3363, National Bureau of Economic Research, Inc.
- Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
- 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).
- Jens Ludwig & Sendhil Mullainathan, 2021.
"Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System,"
Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
- Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," NBER Working Papers 29267, National Bureau of Economic Research, Inc.
Corrections
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:bis:bisifc:46-26. 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: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.