Report NEP-BIG-2021-02-01

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:

• Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
• Mr. Serkan Arslanalp & Mr. Marco Marini & Ms. Patrizia Tumbarello, 2019. "Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time," IMF Working Papers 2019/275, International Monetary Fund.
• Bao Hoang Nguyen & Valentin Zelenyuk, 2020. "PAggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives," CEPA Working Papers Series WP112020, School of Economics, University of Queensland, Australia.
• Mochen Yang & Edward McFowland III & Gordon Burtch & Gediminas Adomavicius, 2020. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," Papers 2012.10790, arXiv.org.
• Jillian M. Clements & Di Xu & Nooshin Yousefi & Dmitry Efimov, 2020. "Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data," Papers 2012.15330, arXiv.org.
• Ali R. Baghirzade, 2020. "Development of cloud, digital technologies and the introduction of chip technologies," Papers 2012.08864, arXiv.org.
• Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021. "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers 917, Bank for International Settlements.
• Nan Hu & Jian Li & Alexis Meyer-Cirkel, 2019. "Completing the Market: Generating Shadow CDS Spreads by Machine Learning," IMF Working Papers 2019/292, International Monetary Fund.
• James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
• Ian Burn & Daniel Firoozi & Daniel Ladd & David Neumark, 2021. "Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment," NBER Working Papers 28328, National Bureau of Economic Research, Inc.
• Gang Huang & Xiaohua Zhou & Qingyang Song, 2020. "Deep reinforcement learning for portfolio management," Papers 2012.13773, arXiv.org, revised Apr 2022.
• Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020. "Long-term prediction intervals with many covariates," Papers 2012.08223, arXiv.org, revised Sep 2021.
• Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020. "Algorithms for Learning Graphs in Financial Markets," Papers 2012.15410, arXiv.org.
• Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
• Oz Shy, 2020. "Alternative Methods for Studying Consumer Payment Choice," FRB Atlanta Working Paper 2020-8, Federal Reserve Bank of Atlanta.
• Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
• Vladimir Vargas-Calder'on & Jorge E. Camargo, 2020. "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy," Papers 2012.09115, arXiv.org.
• Jeffrey Grogger & Ria Ivandic & Tom Kirchmaier, 2020. "In brief...Tackling domestic violence using machine learning," CentrePiece - The magazine for economic performance 579, Centre for Economic Performance, LSE.
• Philip Erickson, 2020. "Identification of inferential parameters in the covariate-normalized linear conditional logit model," Papers 2012.08022, arXiv.org.
• Rauh, C. & Renée, L., 2021. "Parenting Types," Cambridge Working Papers in Economics 2110, Faculty of Economics, University of Cambridge.
• Mesbah, Neda & Tauchert, Christoph & Buxmann, Peter, 2021. "Whose Advice Counts More – Man or Machine? An Experimental Investigation of AI-based Advice Utilization," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124796, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Matias Nehuen Iglesias, 2021. "The Overlooked Insights from Correlation Structures in Economic Geography," Papers in Evolutionary Economic Geography (PEEG) 2105, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2021.
• Budzinski, Oliver & Gänßle, Sophia & Lindstädt-Dreusicke, Nadine, 2021. "Data (r)evolution - The economics of algorithmic search and recommender services," Ilmenau Economics Discussion Papers 148, Ilmenau University of Technology, Institute of Economics.
• Carbonero, Francesco & Scicchitano, Sergio, 2021. "Labour and technology at the time of Covid-19. Can artificial intelligence mitigate the need for proximity?," GLO Discussion Paper Series 765, Global Labor Organization (GLO).
• Badruddoza, Syed & Amin, Modhurima & McCluskey, Jill, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
• Jeffrey D. Michler & Anna Josephson & Talip Kilic & Siobhan Murray, 2020. "Estimating the Impact of Weather on Agriculture," Papers 2012.11768, arXiv.org, revised Oct 2021.
• Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021. "Measuring national happiness with music," The Warwick Economics Research Paper Series (TWERPS) 1326, University of Warwick, Department of Economics.
• Chaikal Nuryakin & Nandaru Annabil Gumelar & Muhammad Dhiya Ul-Haq & Riefhano Patonangi & Andhika Putra Pratama, 2020. "Modernizing Official Statistics with Big Data: A Case on PODES," LPEM FEBUI Working Papers 202045, LPEM, Faculty of Economics and Business, University of Indonesia, revised 2020.
• Enrico Santarelli & Jacopo Staccioli & Marco Vivarelli, 2021. "Robots, AI, and Related Technologies: A Mapping of the New Knowledge Base," DISCE - Quaderni del Dipartimento di Politica Economica dipe0016, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).