Report NEP-BIG-2021-03-15

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• 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.
• Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
• Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.
• Masashi Goto, 2021. "Accepting the Future as Unforeseeable: Sensemaking by Professionals in the Rise of Artificial Intelligence," Discussion Paper Series DP2021-05, Research Institute for Economics & Business Administration, Kobe University.
• Maximilien Germain & Mathieu Lauri`ere & Huy^en Pham & Xavier Warin, 2021. "DeepSets and their derivative networks for solving symmetric PDEs," Papers 2103.00838, arXiv.org, revised Jan 2022.
• J. Ignacio Conde-Ruiz & Juan José Ganuza & Manu Garcia & Luis A. Puch, 2021. "Gender distribution across topics in Top 5 economics journals: A machine learning approach," Economics Working Papers 1771, Department of Economics and Business, Universitat Pompeu Fabra.
• Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
• Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
• Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
• Dautel, Alexander Jakob & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," IRTG 1792 Discussion Papers 2020-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers 2020-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Zijian Shi & Yu Chen & John Cartlidge, 2021. "The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network," Papers 2103.01670, arXiv.org.
• Klaus Gründler & Tommy Krieger, 2021. "Using Machine Learning for Measuring Democracy: An Update," CESifo Working Paper Series 8903, CESifo.
• Best, Katherine Laura & Speyer, Lydia Gabriela & Murray, Aja Louise & Ushakova, Anastasia, 2021. "Prediction of Attrition in Large Longitudinal Studies: Tree-based methods versus Multinomial Logistic Models," SocArXiv tyszr, Center for Open Science.
• Claire Greene & Brian Prescott & Oz Shy, 2021. "How People Pay Each Other: Data, Theory, and Calibrations," FRB Atlanta Working Paper 2021-11, Federal Reserve Bank of Atlanta.
• Bolte, Jérôme & Pauwels, Edouard, 2021. "A mathematical model for automatic differentiation in machine learning," TSE Working Papers 21-1184, Toulouse School of Economics (TSE).
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.
• Andry Alamsyah & Yahya Peranginangin & Gabriel Nurhadi, 2021. "Learning Organization using Conversational Social Network for Social Customer Relationship Management Effort," Papers 2103.06051, arXiv.org.
• Raden Johannes & Andry Alamsyah, 2021. "Sales Prediction Model Using Classification Decision Tree Approach For Small Medium Enterprise Based on Indonesian E-Commerce Data," Papers 2103.03117, arXiv.org.
• Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
• Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
• Wu, Desheng Dang & Härdle, Wolfgang Karl, 2020. "Service Data Analytics and Business Intelligence," IRTG 1792 Discussion Papers 2020-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Jérémy Fouliard & Michael Howell & Hélène Rey, 2021. "Answering the Queen: Machine learning and financial crises," BIS Working Papers 926, Bank for International Settlements.
• Tao Zou & Xian Li & Xuan Liang & Hansheng Wang, 2021. "On the Subbagging Estimation for Massive Data," Papers 2103.00631, arXiv.org.
• Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
• Gary Cornwall & Jeff Chen & Beau Sauley, 2021. "Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?," Papers 2103.01368, arXiv.org.
• MARTENS Bertin, 2020. "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy 2020-09, Joint Research Centre.