Report NEP-BIG-2018-12-24

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Giacomo Caterini, 2018, "Classifying Firms with Text Mining," DEM Working Papers, Department of Economics and Management, number 2018/09.
• Havranek, Tomas & Zeynalov, Ayaz, 2018, "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 187420.
• Kazuyuki MOTOHASHI, 2018, "Understanding AI Driven Innovation by Linked Database of Scientific Articles and Patents," Policy Discussion Papers, Research Institute of Economy, Trade and Industry (RIETI), number 18017, Nov.
• Gregory J. Cohen & Melanie Friedrichs & Kamran Gupta & William Hayes & Seung Jung Lee & W. Blake Marsh & Nathan Mislang & Maya Shaton & Martin Sicilian, 2018, "The U.S. Syndicated Loan Market : Matching Data," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2018-085, Dec, DOI: 10.17016/FEDS.2018.085.
• Gregory J. Cohen & Melanie Friedrichs & Kamran Gupta & William Hayes & Seung Jung Lee & W. Blake Marsh & Nathan Mislang & Maya Shaton & Martin Sicilian, 2018, "The U.S. Syndicated Loan Market: Matching Data," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 18-9, Dec, DOI: 10.18651/RWP2018-09.
• Achref Bachouch & C^ome Hur'e & Nicolas Langren'e & Huyen Pham, 2018, "Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications," Papers, arXiv.org, number 1812.05916, Dec, revised Jan 2020.
• Rodríguez Arosemena, Nicolás, 2018, "The Dominium Mundi Game and the Case for Artificial Intelligence in Economics and the Law," MPRA Paper, University Library of Munich, Germany, number 90560, Dec.
• Henry Stone, 2018, "Calibrating rough volatility models: a convolutional neural network approach," Papers, arXiv.org, number 1812.05315, Dec, revised Jul 2019.
• Atik, Can, 2018, "How 'Big Data' affects competition law analysis in Online Platforms and Agriculture: does one size fit all?," 29th European Regional ITS Conference, Trento 2018, International Telecommunications Society (ITS), number 184928.
• Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018, "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2018-08, Jan.
• Timothée Stumpf-Fétizon & Omiros Papaspiliopoulos & José García-Montalvo, 2018, "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers, Barcelona School of Economics, number 1065, Dec.
• Carlo Fezzi & Luca Mosetti, 2018, "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers, Department of Economics and Management, number 2018/10.
• José Garcia Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018, "Bayesian forecasting of electoral outcomes with new parties' competition," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra, number 1624, Dec.
• Hiroshi KIMOTO & Yuriko SAWATANI & Naho SAITO & Koichi IWAMOTO & Yuta TANOUE & Yusuka INOUE, 2018, "The Development of Digital Technology for IT, IoT, Big Data, and AI in Japan's Fourth Industrial Revolution (Japanese)," Policy Discussion Papers (Japanese), Research Institute of Economy, Trade and Industry (RIETI), number 18019, Dec.
• Yusuke Narita & Shota Yasui & Kohei Yata, 2018, "Efficient Counterfactual Learning from Bandit Feedback," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2155, Dec.