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 (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:

• Timothy Besley & Thiemo Fetzer & Hannes Mueller, 2020, "Terror and Tourism: The Economic Consequences of Media Coverage," Working Papers, Barcelona School of Economics, number 1141, Jan.
• McGaughey, Ewan, 2019, "Will robots automate your job away? Full employment, basic income, and economic democracy," LawRxiv, Center for Open Science, number udbj8, Oct, DOI: 10.31219/osf.io/udbj8.
• Rickard Nyman & Paul Ormerod, 2020, "Understanding the Great Recession Using Machine Learning Algorithms," Papers, arXiv.org, number 2001.02115, Jan.
• Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019, "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP30/19, Jun.
• Xi Chen & Ye Luo & Martin Spindler, 2019, "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers, arXiv.org, number 1912.12867, Dec, revised Jan 2020.
• Aniruddha Dutta & Saket Kumar & Meheli Basu, 2019, "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Papers, arXiv.org, number 1912.11166, Dec.
• Ekaterina Semenova & Ekaterina Perevoshchikova & Alexey Ivanov & Mikhail Erofeev, 2019, "Fairness Meets Machine Learning: Searching For A Better Balance," HSE Working papers, National Research University Higher School of Economics, number WP BRP 93/LAW/2019.
• Nicola Uras & Lodovica Marchesi & Michele Marchesi & Roberto Tonelli, 2020, "Forecasting Bitcoin closing price series using linear regression and neural networks models," Papers, arXiv.org, number 2001.01127, Jan.
• 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, Bank for International Settlements, number 834, Dec.
• Ao Kong & Hongliang Zhu & Robert Azencott, 2019, "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers, arXiv.org, number 1912.07165, Dec.
• 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, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2019-3, Jan.
• Greshake Tzovaras, Bastian & Ball, Mad Price, 2019, "Alternative personal data governance models," MetaArXiv, Center for Open Science, number bthj7, Dec, DOI: 10.31219/osf.io/bthj7.
• Saha, Satabdi & Maiti, Tapabrata, 2019, "Big Data, Data Science and Emerging Analytic tools : Impact in social science," SocArXiv, Center for Open Science, number ft27y, Dec, DOI: 10.31219/osf.io/ft27y.
• Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019, "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers, arXiv.org, number 1912.06307, Dec, revised Feb 2021.
• Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019, "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1256, Dec.
• Kadyrov, Timur & Ignatov, Dmitry I., 2019, "Attribution of Customers’ Actions Based on Machine Learning Approach," MPRA Paper, University Library of Munich, Germany, number 97312, Sep, revised 23 Sep 2019.
• Sven Klaassen & Jannis Kück & Martin Spindler & Victor Chernozhukov, 2019, "Uniform inference in high-dimensional Gaussian graphical models," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP29/19, Jun.
• Semenova, Daria & Temirkaeva, Maria, 2019, "The Comparison of Methods for IndividualTreatment Effect Detection," MPRA Paper, University Library of Munich, Germany, number 97309, Sep, 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, Global Labor Organization (GLO), number 433.
• Boeing, Geoff, 2019, "Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology," SocArXiv, Center for Open Science, number vhrdc, Oct, DOI: 10.31219/osf.io/vhrdc.
• Jay Damask, 2019, "A Consistently Oriented Basis for Eigenanalysis," Papers, arXiv.org, number 1912.12983, Dec.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019, "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper, University Library of Munich, Germany, number 97310, Sep, 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, arXiv.org, number 1912.12864, Dec, revised Dec 2022.
• Item repec:imf:imfwpa:19/272 is not listed on IDEAS anymore

• Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019, "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP21/19, May.
• Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019, "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP32/19, Jun.
• Michael, Friedrich & Ignatov, Dmitry I., 2019, "General Game Playing B-to-B Price Negotiations," MPRA Paper, University Library of Munich, Germany, number 97313, Sep, revised 23 Sep 2019.
• Giovanna Tagliaferri & Daria Scacciatelli & Pierfrancesco Alaimo Di Loro, 2019, "VAT tax gap prediction: a 2-steps Gradient Boosting approach," Papers, arXiv.org, number 1912.03781, Dec, revised Jun 2020.
• March, Christoph, 2019, "The behavioral economics of artificial intelligence: Lessons from experiments with computer players," BERG Working Paper Series, Bamberg University, Bamberg Economic Research Group, number 154.
• 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, arXiv.org, number 1912.04015, Dec, revised Sep 2020.
• Burgess, Robin & Costa, Francisco J M & Olken, Ben, 2019, "The Brazilian Amazon’s Double Reversal of Fortune," SocArXiv, Center for Open Science, number 67xg5, Aug, DOI: 10.31219/osf.io/67xg5.
• 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, CESifo, number 7981.
• Bräuning, Michael & Malikkidou, Despo & Scricco, Giorgio & Scalone, Stefano, 2019, "A new approach to Early Warning Systems for small European banks," Working Paper Series, European Central Bank, number 2348, Dec.
• Kea BARET & Theophilos PAPADIMITRIOU, 2019, "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg, number 2019-48.
• Yixiao ZHOU & Rod TYERS, 2019, "Implications of Automation for Global Migration," Economics Discussion / Working Papers, The University of Western Australia, Department of Economics, number 19-19.
• Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019, "Automatic Financial Feature Construction," Papers, arXiv.org, number 1912.06236, Dec, revised Oct 2020.
• Item repec:imf:imfwpa:19/273 is not listed on IDEAS anymore

• José-Luis Peydró [AP BACKUP – NOW EXTERNAL] & Miguel Boucinha & Carlo Altavilla & Frank Smets & José-Luis Peydró, 2019, "Banking Supervision, Monetary Policy and Risk-Taking: Big Data Evidence from 15 Credit Registers," Working Papers, Barcelona School of Economics, number 1137, Dec.
• Shengli Chen & Zili Zhang, 2019, "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers, arXiv.org, number 1912.11059, Dec.

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 (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: