Report NEP-BIG-2020-11-30

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:

• Krystian Andruszek & Piotr Wójcik, 2020. "Predicting well-being based on features visible from space – the case of Warsaw," Working Papers 2020-37, Faculty of Economic Sciences, University of Warsaw.
• Jiafeng Chen & Daniel L. Chen & Greg Lewis, 2020. "Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models," Papers 2011.06158, arXiv.org, revised Jun 2021.
• Sidra Mehtab & Jaydip Sen & Subhasis Dasgupta, 2020. "Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models," Papers 2011.08011, arXiv.org, revised Jan 2021.
• Shao, Yongtong & Xiong, Tao & Li, Minghao & Hayes, Dermot & Zhang, Wendong & Xie, Wei, 2020. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," ISU General Staff Papers 202001010800001619, Iowa State University, Department of Economics.
• Martin Johnsen & Oliver Brandt & Sergio Garrido & Francisco C. Pereira, 2020. "Population synthesis for urban resident modeling using deep generative models," Papers 2011.06851, arXiv.org.
• Jikhan Jeong, 2020. "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers pje208, Job Market Papers.
• Adam Bouland & Wim van Dam & Hamed Joorati & Iordanis Kerenidis & Anupam Prakash, 2020. "Prospects and challenges of quantum finance," Papers 2011.06492, arXiv.org.
• Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2020. "Data-driven mergers and personalization," ISER Discussion Paper 1108, Institute of Social and Economic Research, Osaka University.
• Schubert, Torben & Jäger, Angela & Türkeli, Serdar & Visentin, Fabiana, 2020. "Addressing the productivity paradox with big data: A literature review and adaptation of the CDM econometric model," MERIT Working Papers 2020-050, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
• Gusarov, N. & Talebijmalabad, A. & Joly, I., 2020. "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers 2020-12, Grenoble Applied Economics Laboratory (GAEL).
• Item repec:spo:wpmain:info:hdl:2441/3mgbd73vkp9f9oje7utooe7vpg is not listed on IDEAS anymore

• Piotr Wójcik & Bartłomiej Wieczorek, 2020. "We have just explained real convergence factors using machine learning," Working Papers 2020-38, Faculty of Economic Sciences, University of Warsaw.
• Paul Hubert & Fabien Labondance, 2019. "Central bank tone and the dispersion of views within monetary policy committees," SciencePo Working papers Main hal-03403256, HAL.
• Lijuan Huo & Jin Seo Cho, 2020. "Sequentially Estimating Approximate Conditional Mean Using the Extreme Learning Machine," Working papers 2020rwp-180, Yonsei University, Yonsei Economics Research Institute.
• Shawn K. McGuire & Charles B. Delahunt, 2020. "Predicting United States Policy Outcomes with Random Forests," Working Papers Series inetwp138, Institute for New Economic Thinking.
• Juan J Dolado & Florentino Felgueroso & Juan F.Jimeno, 2020. "Past, Present and Future of the Spanish Labour Market: When the Pandemic meets the Megatrends," Studies on the Spanish Economy eee2020-37, FEDEA.
• Xingchen Wan & Jie Yang & Slavi Marinov & Jan-Peter Calliess & Stefan Zohren & Xiaowen Dong, 2020. "Sentiment Correlation in Financial News Networks and Associated Market Movements," Papers 2011.06430, arXiv.org, revised Feb 2021.