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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 (Tom Coupe) issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, Bank for International Settlements, number 930, Mar.
- 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, Banco de España, number 2105, Jan.
- Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021, "Explainable AI in Credit Risk Management," Papers, arXiv.org, number 2103.00949, Mar.
- Masashi Goto, 2021, "Accepting the Future as Unforeseeable: Sensemaking by Professionals in the Rise of Artificial Intelligence," Discussion Paper Series, Research Institute for Economics & Business Administration, Kobe University, number DP2021-05, Mar.
- Maximilien Germain & Mathieu Lauri`ere & Huy^en Pham & Xavier Warin, 2021, "DeepSets and their derivative networks for solving symmetric PDEs," Papers, arXiv.org, number 2103.00838, Mar, 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, Department of Economics and Business, Universitat Pompeu Fabra, number 1771, Feb.
- Xingcai Zhou & Jiangyan Wang, 2021, "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers, arXiv.org, number 2103.00711, Feb.
- Henri Fraisse & Matthias Laporte, 2021, "Return on Investment on AI: The Case of Capital Requirement," Working papers, Banque de France, number 809.
- Kristof Lommers & Ouns El Harzli & Jack Kim, 2021, "Confronting Machine Learning With Financial Research," Papers, arXiv.org, number 2103.00366, Feb, 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, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-006.
- Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020, "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-013.
- 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, arXiv.org, number 2103.01670, Mar.
- Klaus Gründler & Tommy Krieger, 2021, "Using Machine Learning for Measuring Democracy: An Update," CESifo Working Paper Series, CESifo, number 8903.
- 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, Center for Open Science, number tyszr, Mar, DOI: 10.31219/osf.io/tyszr.
- Claire Greene & Brian Prescott & Oz Shy, 2021, "How People Pay Each Other: Data, Theory, and Calibrations," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2021-11, Feb, DOI: 10.29338/wp2021-11.
- Bolte, Jérôme & Pauwels, Edouard, 2021, "A mathematical model for automatic differentiation in machine learning," TSE Working Papers, Toulouse School of Economics (TSE), number 21-1184, Feb.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," Papers, arXiv.org, number 2103.01201, Mar.
- Andry Alamsyah & Yahya Peranginangin & Gabriel Nurhadi, 2021, "Learning Organization using Conversational Social Network for Social Customer Relationship Management Effort," Papers, arXiv.org, number 2103.06051, Mar.
- Raden Johannes & Andry Alamsyah, 2021, "Sales Prediction Model Using Classification Decision Tree Approach For Small Medium Enterprise Based on Indonesian E-Commerce Data," Papers, arXiv.org, number 2103.03117, Mar.
- Philippe Goulet Coulombe, 2021, "Slow-Growing Trees," Papers, arXiv.org, number 2103.01926, Mar, revised Jul 2021.
- Stamer, Vincent, 2021, "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers, Kiel Institute for the World Economy, number 2179.
- Wu, Desheng Dang & Härdle, Wolfgang Karl, 2020, "Service Data Analytics and Business Intelligence," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-002.
- Jérémy Fouliard & Michael Howell & Hélène Rey, 2021, "Answering the Queen: Machine learning and financial crises," BIS Working Papers, Bank for International Settlements, number 926, Feb.
- Tao Zou & Xian Li & Xuan Liang & Hansheng Wang, 2021, "On the Subbagging Estimation for Massive Data," Papers, arXiv.org, number 2103.00631, Feb.
- Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021, "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg, number 2021-01.
- Gary Cornwall & Jeff Chen & Beau Sauley, 2021, "Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?," Papers, arXiv.org, number 2103.01368, Mar.
- MARTENS Bertin, 2020, "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy, Joint Research Centre, number 2020-09, Dec.