Report NEP-BIG-2020-01-13

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:

• Timothy Besley & Thiemo Fetzer & Hannes Mueller, 2020. "Terror and Tourism: The Economic Consequences of Media Coverage," Working Papers 1141, Barcelona School of Economics.
• McGaughey, Ewan, 2019. "Will robots automate your job away? Full employment, basic income, and economic democracy," LawArXiv udbj8, Center for Open Science.
• Rickard Nyman & Paul Ormerod, 2020. "Understanding the Great Recession Using Machine Learning Algorithms," Papers 2001.02115, arXiv.org.
• Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
• Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
• Aniruddha Dutta & Saket Kumar & Meheli Basu, 2019. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Papers 1912.11166, arXiv.org.
• Ekaterina Semenova & Ekaterina Perevoshchikova & Alexey Ivanov & Mikhail Erofeev, 2019. "Fairness Meets Machine Learning: Searching For A Better Balance," HSE Working papers WP BRP 93/LAW/2019, National Research University Higher School of Economics.
• Nicola Uras & Lodovica Marchesi & Michele Marchesi & Roberto Tonelli, 2020. "Forecasting Bitcoin closing price series using linear regression and neural networks models," Papers 2001.01127, arXiv.org.
• Leonardo Gambacorta & Yiping Huang & Han Qiu & Jingyi Wang, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," BIS Working Papers 834, Bank for International Settlements.
• Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
• Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
• Greshake Tzovaras, Bastian & Ball, Mad Price, 2019. "Alternative personal data governance models," MetaArXiv bthj7, Center for Open Science.
• Saha, Satabdi & Maiti, Tapabrata, 2019. "Big Data, Data Science and Emerging Analytic tools : Impact in social science," SocArXiv ft27y, Center for Open Science.
• Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
• Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
• Kadyrov, Timur & Ignatov, Dmitry I., 2019. "Attribution of Customers’ Actions Based on Machine Learning Approach," MPRA Paper 97312, University Library of Munich, Germany, revised 23 Sep 2019.
• Sven Klaassen & Jannis Kück & Martin Spindler & Victor Chernozhukov, 2019. "Uniform inference in high-dimensional Gaussian graphical models," CeMMAP working papers CWP29/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
• Semenova, Daria & Temirkaeva, Maria, 2019. "The Comparison of Methods for IndividualTreatment Effect Detection," MPRA Paper 97309, University Library of Munich, Germany, revised 23 Sep 2019.
• Van Roy, Vincent & Vertesy, Daniel & Damioli, Giacomo, 2019. "AI and Robotics Innovation: a Sectoral and Geographical Mapping using Patent Data," GLO Discussion Paper Series 433, Global Labor Organization (GLO).
• Boeing, Geoff, 2019. "Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology," SocArXiv vhrdc, Center for Open Science.
• Jay Damask, 2019. "A Consistently Oriented Basis for Eigenanalysis," Papers 1912.12983, arXiv.org.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019. "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper 97310, University Library of Munich, Germany, revised 23 Sep 2019.
• Bart Cockx & Michael Lechner & Joost Bollens, 2019. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Papers 1912.12864, arXiv.org, revised Dec 2022.
• Anil Ari & Sophia Chen & Lev Ratnovski, 2019. "The Dynamics of Non-Performing Loans during Banking Crises: A New Database," IMF Working Papers 19/272, International Monetary Fund.
• Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019. "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers CWP21/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
• Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
• Michael, Friedrich & Ignatov, Dmitry I., 2019. "General Game Playing B-to-B Price Negotiations," MPRA Paper 97313, University Library of Munich, Germany, revised 23 Sep 2019.
• Giovanna Tagliaferri & Daria Scacciatelli & Pierfrancesco Alaimo Di Loro, 2019. "VAT tax gap prediction: a 2-steps Gradient Boosting approach," Papers 1912.03781, arXiv.org, revised Jun 2020.
• March, Christoph, 2019. "The behavioral economics of artificial intelligence: Lessons from experiments with computer players," BERG Working Paper Series 154, Bamberg University, Bamberg Economic Research Group.
• Somayeh Kokabisaghi & Mohammadesmaeil Ezazi & Reza Tehrani & Nourmohammad Yaghoubi, 2019. "Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices," Papers 1912.04015, arXiv.org, revised Sep 2020.
• Burgess, Robin & Costa, Francisco J M & Olken, Ben, 2019. "The Brazilian Amazon’s Double Reversal of Fortune," SocArXiv 67xg5, Center for Open Science.
• Leandro Medina & Friedrich Schneider, 2019. "Shedding Light on the Shadow Economy: A Global Database and the Interaction with the Official One," CESifo Working Paper Series 7981, CESifo.
• Bräuning, Michael & Malikkidou, Despo & Scricco, Giorgio & Scalone, Stefano, 2019. "A new approach to Early Warning Systems for small European banks," Working Paper Series 2348, European Central Bank.
• Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
• Yixiao ZHOU & Rod TYERS, 2019. "Implications of Automation for Global Migration," Economics Discussion / Working Papers 19-19, The University of Western Australia, Department of Economics.
• Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019. "Automatic Financial Feature Construction," Papers 1912.06236, arXiv.org, revised Oct 2020.
• Chengyu Huang & Sean Simpson & Daria Ulybina & Agustin Roitman, 2019. "News-based Sentiment Indicators," IMF Working Papers 19/273, International Monetary Fund.
• Carlo Altavilla & Miguel Boucinha & José-Luis Peydró & Frank Smets, 2019. "Banking Supervision, Monetary Policy and Risk-Taking: Big Data Evidence from 15 Credit Registers," Working Papers 1137, Barcelona School of Economics.
• Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.