Improving data quality and closing data gaps with machine learning
In: Data needs and Statistics compilation for macroprudential analysis
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References listed on IDEAS
- Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
- 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.
- 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).
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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,
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- José María Serena Garralda & Bruno Tissot, 2018. "Central banks and trade repositories derivatives data," IFC Reports 7, Bank for International Settlements.
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