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Report NEP-CMP-2021-03-15
This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-CMP
The following items were announced in this report:
- 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.
- 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.
- 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.
- Xingcai Zhou & Jiangyan Wang, 2021, "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers, arXiv.org, number 2103.00711, Feb.
- Baptiste Barreau & Laurent Carlier, 2020, "History-Augmented Collaborative Filtering for Financial Recommendations," Post-Print, HAL, number hal-03144669, Sep, DOI: 10.1145/3383313.3412206.
- 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.
- 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.
- Erik Heilmann & Andreas Zeiselmair & Thomas Estermann, 2021, "Matching supply and demand of electricity network-supportive flexibility: A case study with three comprehensible matching algorithms," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202110.
- Yongyang Cai & Kenneth L. Judd, 2021, "A Simple but Powerful Simulated Certainty Equivalent Approximation Method for Dynamic Stochastic Problems," NBER Working Papers, National Bureau of Economic Research, Inc, number 28502, Feb.
- 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.
- Philippe Goulet Coulombe, 2021, "Slow-Growing Trees," Papers, arXiv.org, number 2103.01926, Mar, revised Jul 2021.
- Nicholas Moehle & Jack Gindi & Stephen Boyd & Mykel Kochenderfer, 2021, "Portfolio Construction as Linearly Constrained Separable Optimization," Papers, arXiv.org, number 2103.05455, Mar, revised Jul 2022.
- Morris A. Davis & Jesse M. Gregory & Daniel A. Hartley & Kegon T.K. Tan, 2021, "Neighborhood Effects and Housing Vouchers," NBER Working Papers, National Bureau of Economic Research, Inc, number 28508, Feb.
- 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.
- 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.
- M. Avellaneda & T. N. Li & A. Papanicolaou & G. Wang, 2021, "Trading Signals In VIX Futures," Papers, arXiv.org, number 2103.02016, Mar, revised Nov 2021.
- Furkan Gursoy & Bertan Badur, 2021, "An Agent-Based Modelling Approach to Brain Drain," Papers, arXiv.org, number 2103.03234, Mar, revised Mar 2021.
- 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.
- Doherr, Thorsten, 2021, "Disambiguation by namesake risk assessment," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 21-021.
- Nikita Kozodoi & Johannes Jacob & Stefan Lessmann, 2021, "Fairness in Credit Scoring: Assessment, Implementation and Profit Implications," Papers, arXiv.org, number 2103.01907, Mar, revised Jun 2022.
- Shota Imaki & Kentaro Imajo & Katsuya Ito & Kentaro Minami & Kei Nakagawa, 2021, "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging," Papers, arXiv.org, number 2103.01775, Mar.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021, "Can Machine Learning Catch the COVID-19 Recession?," Papers, arXiv.org, number 2103.01201, Mar.
- Kristof Lommers & Ouns El Harzli & Jack Kim, 2021, "Confronting Machine Learning With Financial Research," Papers, arXiv.org, number 2103.00366, Feb, revised Mar 2021.
- Henry Hanifan & Ben Watson & John Cartlidge & Dave Cliff, 2021, "Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders," Papers, arXiv.org, number 2103.00600, Feb.
- 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.
- Spilak, Bruno & Härdle, Wolfgang Karl, 2020, "Tail-risk protection: Machine Learning meets modern Econometrics," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-015.
- 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.
- 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.
- Henri Fraisse & Matthias Laporte, 2021, "Return on Investment on AI: The Case of Capital Requirement," Working papers, Banque de France, number 809.
- 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.
- 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.
- 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.
- Klaus Gründler & Tommy Krieger, 2021, "Using Machine Learning for Measuring Democracy: An Update," CESifo Working Paper Series, CESifo, number 8903.
- Tao Zou & Xian Li & Xuan Liang & Hansheng Wang, 2021, "On the Subbagging Estimation for Massive Data," Papers, arXiv.org, number 2103.00631, Feb.
- Jacques Bughin & Michele Cincera & Kelly Peters & Dorota Reykowska & Marcin Zyszkiewicz & Rafal Ohme, 2021, "Make it or Break it: Vaccination Intention at the Time of Covid-19," Working Papers TIMES², ULB -- Universite Libre de Bruxelles, number 2021-043, Jan.
- Pavlova, Elitsa & Signore, Simone, 2021, "The European venture capital landscape: An EIF perspective. Volume VI: The impact of VC on the exit and innovation outcomes of EIF-backed start-ups," EIF Working Paper Series, European Investment Fund (EIF), number 2021/70.