Report NEP-BIG-2019-05-27

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:

• Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
• Ho Fai Chan & Bruno S. Frey & Ahmed Skali & Benno Torgler, 2019. "Political Entrenchment and GDP Misreporting," CREMA Working Paper Series 2019-02, Center for Research in Economics, Management and the Arts (CREMA).
• Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - The Institute for Economic Development Working Papers Series dp-298, Boston University - Department of Economics.
• Sudiksha Joshi, 2019. "Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model," Papers 1905.07848, arXiv.org.
• Shangeth Rajaa & Jajati Keshari Sahoo, 2019. "Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction," Papers 1905.07581, arXiv.org.
• Reaz Chowdhury & M. Arifur Rahman & M. Sohel Rahman & M. R. C. Mahdy, 2019. "Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning," Papers 1905.08444, arXiv.org.
• Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
• Heinisch, Dominik & Koenig, Johannes & Otto, Anne, 2019. "The IAB-INCHER project of earned doctorates (IIPED): A supervised machine learning approach to identify doctorate recipients in the German integrated employment biography data," IAB-Discussion Paper 201913, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
• Lisa R. Goldberg & Saad Mouti, 2019. "Sustainable Investing and the Cross-Section of Returns and Maximum Drawdown," Papers 1905.05237, arXiv.org, revised Dec 2023.
• Ludovic Gouden`ege & Andrea Molent & Antonino Zanette, 2019. "Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian models," Papers 1905.09474, arXiv.org, revised Jun 2019.
• de Kok, Ties, 2019. "Essays on reporting and information processing," Other publications TiSEM 468fd12b-19c0-4c7b-a33a-6, Tilburg University, School of Economics and Management.
• Christopher Kath & Florian Ziel, 2019. "Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets," Papers 1905.07886, arXiv.org, revised Sep 2020.
• Samuel Asante Gyamerah & Philip Ngare & Dennis Ikpe, 2019. "Hedging crop yields against weather uncertainties -- a weather derivative perspective," Papers 1905.07546, arXiv.org, revised Aug 2019.
• Arthur Turrell & Bradley J. Speigner & Jyldyz Djumalieva & David Copple & James Thurgood, 2019. "Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings," NBER Working Papers 25837, National Bureau of Economic Research, Inc.
• Wu, Guoyuan & Ye, Fei & Hao, Peng & Esaid, Danial & Boriboonsomsin, Kanok & Barth, Matthew J., 2019. "Deep Learning–based Eco-driving System for Battery Electric Vehicles," Institute of Transportation Studies, Working Paper Series qt9fz140zt, Institute of Transportation Studies, UC Davis.