Report NEP-BIG-2019-02-11

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:

• Adam Hale Shapiro & Daniel J. Wilson, 2021. "Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis," Working Paper Series 2019-2, Federal Reserve Bank of San Francisco.
• Hunt Allcott & Luca Braghieri & Sarah Eichmeyer & Matthew Gentzkow, 2019. "The Welfare Effects of Social Media," NBER Working Papers 25514, National Bureau of Economic Research, Inc.
• Faisal I Qureshi, 2018. "Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach," Papers 1901.10534, arXiv.org.
• Frondel, Manuel & Kussel, Gerhard & Sommer, Stephan & Vance, Colin, 2019. "Local cost for global benefit: The case of wind turbines," Ruhr Economic Papers 791, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, revised 2019.
• Leonardo Felizardo & Afonso Pinto, 2019. "A Study on Neural Network Architecture Applied to the Prediction of Brazilian Stock Returns," Papers 1901.09143, arXiv.org.
• Stephany, Fabian, 2018. "It is not only size that matters: How unique is the Estonian e-governance success story?," Working Papers 15, Agenda Austria.
• Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing options and computing implied volatilities using neural networks," Papers 1901.08943, arXiv.org, revised Apr 2019.
• Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.
• Karlson Pfannschmidt & Pritha Gupta & Bjorn Haddenhorst & Eyke Hullermeier, 2019. "Learning Context-Dependent Choice Functions," Papers 1901.10860, arXiv.org, revised Oct 2021.
• Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E, 2019. "lassopack: Model Selection and Prediction with Regularized Regression in Stata," IZA Discussion Papers 12081, Institute of Labor Economics (IZA).
• Brühl, Volker, 2019. "Big Data, Data Mining, Machine Learning und Predictive Analytics: Ein konzeptioneller Überblick," CFS Working Paper Series 617, Center for Financial Studies (CFS).