Report NEP-BIG-2020-07-27

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:

• Gambacorta, Leonardo & Huang, Yiping & Qiu, Han & Wang, Jingyi, 2019, "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14259, Dec.
• Roberto Molinari & Gaetan Bakalli & Stéphane Guerrier & Cesare Miglioli & Samuel Orso & O. Scaillet, 2020, "Swag: A Wrapper Method for Sparse Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-49, Jun.
• Marijn A. Bolhuis & Brett Rayner, 2020, "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers, International Monetary Fund, number 2020/045, Feb.
• Marijn A. Bolhuis & Brett Rayner, 2020, "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers, International Monetary Fund, number 2020/044, Feb.
• Martin, Ian & Nagel, Stefan, 2019, "Market Efficiency in the Age of Big Data," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14235, Dec.
• Azar, José & Alekseeva, Liudmila & Gine, Mireia & Samila, Sampsa & Taska, Bledi, 2020, "The Demand for AI Skills in the Labor Market," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14320, Jan.
• E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020, "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers, arXiv.org, number 2006.16383, Jun, revised Aug 2020.
• Daniel Bartl & Samuel Drapeau & Jan Obloj & Johannes Wiesel, 2020, "Sensitivity analysis of Wasserstein distributionally robust optimization problems," Papers, arXiv.org, number 2006.12022, Jun, revised Nov 2021.
• Susan Ariel Aaronson, 2020, "America's uneven approach to AI and its consequences," Working Papers, The George Washington University, Institute for International Economic Policy, number 2020-7, Jul.
• Cyrille Lenoel & Garry Young, 2020, "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2020-05, Apr.
• Ali Hirsa & Weilong Fu, 2020, "An unsupervised deep learning approach in solving partial integro-differential equations," Papers, arXiv.org, number 2006.15012, Jun, revised Dec 2020.
• Duso, Tomaso & Argentesi, Elena & Buccirossi, Paolo & Calvano, Emilio & Marrazzo, Alessia & Nava, Salvatore, 2019, "Merger Policy in Digital Markets: An Ex-Post Assessment," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14166, Dec.
• Philippe Goulet Coulombe, 2020, "The Macroeconomy as a Random Forest," Papers, arXiv.org, number 2006.12724, Jun, revised Mar 2021.
• Lechner, Michael & Cockx, Bart & Bollens, Joost, 2020, "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14270, Jan.
• David Zenz, 2020, "Die Vernetzung Wiens mit den Städten Europas," wiiw Statistical Reports, The Vienna Institute for International Economic Studies, wiiw, number 9, Jun.
• Kwadwo Osei Bonsu & Jie Song, 2020, "Turbulence on the Global Economy influenced by Artificial Intelligence and Foreign Policy Inefficiencies," Papers, arXiv.org, number 2006.16911, Jun.
• Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020, "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14266, Jan.
• Jungsik Hwang, 2020, "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers, arXiv.org, number 2007.06848, Jul.
• Hassan, Tarek & Hollander, Stephan & van Lent, Laurence & Tahoun, Ahmed, 2019, "The Global Impact of Brexit Uncertainty," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14253, Dec.
• Jonathan Readshaw & Stefano Giani, 2020, "Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations," Papers, arXiv.org, number 2006.12426, Jun.
• Jozef Barunik & Michael Ellington, 2020, "Persistence in Financial Connectedness and Systemic Risk," Papers, arXiv.org, number 2007.07842, Jul, revised Nov 2023.
• Pai, Mallesh & Hansen, Karsten, 2020, "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14372, Jan.
• Dainis Zegners & Uwe Sunde & Anthony Strittmatter, 2020, "Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach," CESifo Working Paper Series, CESifo, number 8341.
• Massimo Guidolin & Manuela Pedio, 2020, "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 20145.
• Gorshkova, Taisiya (Горшкова, Таисия) & Turuntseva, Marina (Турунцева, Марина), 2020, "Theoretical approaches to forecasting regional macro-indicators
  [Теоретические Подходы К Прогнозированию Региональных Макропоказателей]," Working Papers, Russian Presidential Academy of National Economy and Public Administration, number 032042, Mar.
• Mr. Serhan Cevik, 2020, "Where Should We Go? Internet Searches and Tourist Arrivals," IMF Working Papers, International Monetary Fund, number 2020/022, Jan.
• Badr Bentalha, 2020, "Big-Data and Service Supply chain management: Challenges and opportunities
  [Big-Data et Service Supply chain management: Challenges et opportunités]," Post-Print, HAL, number hal-02680861, DOI: 10.5281/zenodo.3607357.
• Laurent Ferrara & Anna Simoni, 2020, "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers, arXiv.org, number 2007.00273, Jul, revised Sep 2022.
• Osório, António (António Miguel) & Pinto, Alberto Adrego, 2019, "Information, uncertainty and the manipulability of artifcial intelligence autonomous vehicles systems," Working Papers, Universitat Rovira i Virgili, Department of Economics, number 2072/376028.
• Mels de Zeeuw, 2020, "Opportunity Occupations and the Future of Work," Workforce Currents, Federal Reserve Bank of Atlanta, number 2020-01, Feb, DOI: 10.29338/wc2020-01.
• Braesemann, Fabian & Stephany, Fabian, 2020, "Measuring Digital Development with Online Data: Digital Economies in Eastern Europe and Central Asia," SocArXiv, Center for Open Science, number f9jqh, Jun, DOI: 10.31219/osf.io/f9jqh.

Report NEP-BIG-2020-07-27

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: