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 (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

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, arXiv.org, number 2101.00878, Jan.
• Mr. Serkan Arslanalp & Mr. Marco Marini & Ms. Patrizia Tumbarello, 2019, "Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time," IMF Working Papers, International Monetary Fund, number 2019/275, Dec.
• 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, School of Economics, University of Queensland, Australia, number WP112020, Dec.
• 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, arXiv.org, number 2012.10790, Dec.
• Jillian M. Clements & Di Xu & Nooshin Yousefi & Dmitry Efimov, 2020, "Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data," Papers, arXiv.org, number 2012.15330, Dec.
• Ali R. Baghirzade, 2020, "Development of cloud, digital technologies and the introduction of chip technologies," Papers, arXiv.org, number 2012.08864, Dec.
• 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, Bank for International Settlements, number 917, Jan.
• Nan Hu & Jian Li & Alexis Meyer-Cirkel, 2019, "Completing the Market: Generating Shadow CDS Spreads by Machine Learning," IMF Working Papers, International Monetary Fund, number 2019/292, Dec.
• James Chapman & Ajit Desai, 2021, "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers, Bank of Canada, number 21-2, Jan, DOI: 10.34989/swp-2021-2.
• 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, National Bureau of Economic Research, Inc, number 28328, Jan.
• Gang Huang & Xiaohua Zhou & Qingyang Song, 2020, "Deep Reinforcement Learning for Long-Short Portfolio Optimization," Papers, arXiv.org, number 2012.13773, Dec, revised Mar 2025.
• Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020, "Long-term prediction intervals with many covariates," Papers, arXiv.org, number 2012.08223, Dec, revised Sep 2021.
• Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020, "Algorithms for Learning Graphs in Financial Markets," Papers, arXiv.org, number 2012.15410, Dec.
• Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020, "Adversarial Estimation of Riesz Representers," Papers, arXiv.org, number 2101.00009, Dec, revised Apr 2024.
• Oz Shy, 2020, "Alternative Methods for Studying Consumer Payment Choice," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2020-8, Jun, DOI: 10.29338/wp2020-08.
• Niko Hauzenberger & Florian Huber & Karin Klieber, 2020, "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers, arXiv.org, number 2012.08155, Dec, 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, arXiv.org, number 2012.09115, Nov.
• Jeffrey Grogger & Ria Ivandic & Tom Kirchmaier, 2020, "In brief...Tackling domestic violence using machine learning," CentrePiece - The magazine for economic performance, Centre for Economic Performance, LSE, number 579, Jul.
• Philip Erickson, 2020, "Identification of inferential parameters in the covariate-normalized linear conditional logit model," Papers, arXiv.org, number 2012.08022, Dec.
• Rauh, C. & Renée, L., 2021, "Parenting Types," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2110, Jan.
• 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), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 124796, Jan.
• Matias Nehuen Iglesias, 2021, "The Overlooked Insights from Correlation Structures in Economic Geography," Papers in Evolutionary Economic Geography (PEEG), Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, number 2105, Jan, 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, Ilmenau University of Technology, Institute of Economics, number 148.
• 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, Global Labor Organization (GLO), number 765.
• Syed Badruddoza & Modhurima Amin & Jill McCluskey, 2019, "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers, School of Economic Sciences, Washington State University, number 2019-5, Dec.
• Jeffrey D. Michler & Anna Josephson & Talip Kilic & Siobhan Murray, 2020, "Estimating the Impact of Weather on Agriculture," Papers, arXiv.org, number 2012.11768, Dec, revised Oct 2021.
• Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021, "Measuring national happiness with music," The Warwick Economics Research Paper Series (TWERPS), University of Warwick, Department of Economics, number 1326.
• 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, LPEM, Faculty of Economics and Business, University of Indonesia, number 202045, revised 2020.
• Enrico Santarelli & Jacopo Staccioli & Marco Vivarelli, 2021, "Robots, AI, and Related Technologies: A Mapping of the New Knowledge Base," DISCE - Working Papers del Dipartimento di Politica Economica, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE), number dipe0016, Jan.

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 (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: