Report NEP-BIG-2019-06-24

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:

• Michael Allan Ribers & Hannes Ullrich, 2019. "Battling antibiotic resistance: can machine learning improve prescribing?," CESifo Working Paper Series 7654, CESifo.
• Mitnik, Oscar A. & Sanchez, Raul & Yañez, Patricia, 2018. "Bright Investments: Measuring the Impact of Transport Infrastructure Using Luminosity Data in Haiti," IDB Publications (Working Papers) 28, Inter-American Development Bank.
• Jillian Grennan & Roni Michaely, 2019. "FinTechs and the Market for Financial Analysis," Swiss Finance Institute Research Paper Series 19-10, Swiss Finance Institute, revised Apr 2019.
• Simon Schnurch & Andreas Wagner, 2019. "Machine Learning on EPEX Order Books: Insights and Forecasts," Papers 1906.06248, arXiv.org, revised Sep 2019.
• Laura Palagi & Ruggiero Seccia, 2019. "Online Block Layer Decomposition schemes for training Deep Neural Networks," DIAG Technical Reports 2019-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
• Giovanni Barone-Adesi & Antonietta Mira & Matteo Pisati, 2019. "The Keys of Predictability: A Comprehensive Study," Swiss Finance Institute Research Paper Series 19-15, Swiss Finance Institute, revised Apr 2019.
• Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Working Papers 25899, National Bureau of Economic Research, Inc.
• Suyong Song & Stephen S. Baek, 2019. "Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship," Papers 1906.06747, arXiv.org.
• Pierrick Piette, 2019. "Can Satellite Data Forecast Valuable Information from USDA Reports ? Evidences on Corn Yield Estimates," Working Papers hal-02149355, HAL.
• Gill Newton, 2019. "Mining Family History Society Burials," Working Papers 34, Department of Economic and Social History at the University of Cambridge, revised 13 Jun 2019.
• André Binette & Karyne B. Charbonneau & Nicholas Curtis & Gabriela Galassi & Scott Counts & Justin Cranshaw, 2019. "Online Job Seekers in Canada: What Can We Learn from Bing Job Queries?," Staff Analytical Notes 2019-18, Bank of Canada.
• Wendy C.Y. LI & NIREI Makoto & YAMANA Kazufumi, 2019. "Value of Data: There's No Such Thing as a Free Lunch in the Digital Economy," Discussion papers 19022, Research Institute of Economy, Trade and Industry (RIETI).
• Jozef Barunik & Cathy Yi-Hsuan Chen & Jan Vecer, 2019. "Sentiment-Driven Stochastic Volatility Model: A High-Frequency Textual Tool for Economists," Papers 1906.00059, arXiv.org.