Report NEP-BIG-2021-04-26

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:

• Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
• Berthine Nyunga Mpinda & Jules Sadefo-Kamdem & Salomey Osei & Jeremiah Fadugba, 2021. "Accuracies of Model Risks in Finance using Machine Learning," Working Papers hal-03191437, HAL.
• Ekaterina Zolotareva, 2021. "Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model," Papers 2104.09341, arXiv.org.
• Kin G. Olivares & Cristian Challu & Grzegorz Marcjasz & Rafal Weron & Artur Dubrawski, 2021. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," WORking papers in Management Science (WORMS) WORMS/21/07, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
• Oscar Claveria & Enric Monte & Salvador Torra, 2021. "“Nowcasting and forecasting GDP growth with machine-learning sentiment indicators”," AQR Working Papers 202101, University of Barcelona, Regional Quantitative Analysis Group, revised Feb 2021.
• Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
• Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
• Kim, Jae Yeon, 2021. "Power, Hate Speech, Machine Learning, and Intersectional Approach," SocArXiv chvgp, Center for Open Science.
• Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
• Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," IZA Discussion Papers 14259, Institute of Labor Economics (IZA).
• Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
• Salomey Osei & Berthine Nyunga Mpinda & Jules Sadefo-Kamdem & Jeremiah Fadugba, 2021. "Accuracies of some Learning or Scoring Models for Credit Risk Measurement," Working Papers hal-03194081, HAL.
• Тулеуов Олжас // Tuleuov Olzhas & Ержан Ислам // Yerzhan Islam & Сейдахметов Ансар // Seidakhmetov Ansar, 2021. "Система Galymzhan: online-оценка потребительской инфляции в Казахстане // Galymzhan System: Online Assessment of Consumer Inflation in Kazakhstan," Working Papers #2021-2, National Bank of Kazakhstan.
• Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.
• Ramiro Emmanuel Jorge, 2020. "Herramientas de Google para la predicción de variables económicas. Una aplicación al Índice Compuesto Coincidente de Actividad Económica de la Provincia de Santa Fe (ICASFe)," Asociación Argentina de Economía Política: Working Papers 4360, Asociación Argentina de Economía Política.
• Mingli Chen & Andreas Joseph & Michael Kumhof & Xinlei Pan & Xuan Zhou, 2021. "Deep Reinforcement Learning in a Monetary Model," Papers 2104.09368, arXiv.org, revised Jan 2023.
• Davcheva, Elena, 2021. "Applications of Machine Learning in Mental Healthcare," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 126173, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Sylvia Klosin, 2021. "Automatic Double Machine Learning for Continuous Treatment Effects," Papers 2104.10334, arXiv.org.
• Guanghua Chi & Han Fang & Sourav Chatterjee & Joshua E. Blumenstock, 2021. "Micro-Estimates of Wealth for all Low- and Middle-Income Countries," Papers 2104.07761, arXiv.org.
• Jiawen Luo & Riza Demirer & Rangan Gupta & Qiang Ji, 2021. "Forecasting Oil and Gold Volatilities with Sentiment Indicators Under Structural Breaks," Working Papers 202130, University of Pretoria, Department of Economics.
• Dempsey, Mark & McBride, Keegan & Bryson, Joanna J., 2021. "The Current State of AI Governance – An EU Perspective," SocArXiv xu3jr, Center for Open Science.
• Zabrocki, Léo & Leroutier, Marion & Bind, Marie-Abèle, 2021. "Estimating the Causal Effects of Cruise Traffic on Air Pollution using Randomization-Based Inference," OSF Preprints v7ctk, Center for Open Science.
• Torti, Francesca & Corbellini, Aldo & Atkinson, Anthony C., 2021. "fsdaSAS: a package for robust regression for very large datasets including the batch forward search," LSE Research Online Documents on Economics 109895, London School of Economics and Political Science, LSE Library.
• José Joaquín Endara, 2020. "Refugee influx and economic activity: evidence from Rohingya refugee camps in Bangladesh," Asociación Argentina de Economía Política: Working Papers 4341, Asociación Argentina de Economía Política.
• Dueñas, Marco & Ortiz, Víctor & Riccaboni, Massimo & Serti, Francesco, 2021. "Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis," Working papers 79, Red Investigadores de Economía.
• Marc-Aurèle Divernois & Damir Filipović, 2021. "Event studies on investor sentiment," Swiss Finance Institute Research Paper Series 21-33, Swiss Finance Institute.
• James Fudurich & Lena Suchanek & Lise Pichette, 2021. "Adoption of digital technologies: Insights from a global survey initiative," Discussion Papers 2021-7, Bank of Canada.
• Roberta Scaramozzino & Paola Cerchiello & Tomaso Aste, 2021. "Information theoretic causality detection between financial and sentiment data," DEM Working Papers Series 202, University of Pavia, Department of Economics and Management.