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Report NEP-BIG-2020-02-03
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:
- Joshua Angrist & Brigham Frandsen, 2019, "Machine Labor," NBER Working Papers, National Bureau of Economic Research, Inc, number 26584, Dec.
- Ian Martin & Stefan Nagel, 2019, "Market Efficiency in the Age of Big Data," NBER Working Papers, National Bureau of Economic Research, Inc, number 26586, Dec.
- Bertani, Filippo & Raberto, Marco & Teglio, Andrea, 2020, "The Productivity and Unemployment Effects of the Digital Transformation: an Empirical and Modelling Assessment," MPRA Paper, University Library of Munich, Germany, number 98233, Jan.
- Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020, "LASSO DEA for small and big data," CEPA Working Papers Series, School of Economics, University of Queensland, Australia, number WP022020, Jan.
- Angela Rita Provenzano & Daniele Trifir`o & Nicola Jean & Giacomo Le Pera & Maurizio Spadaccino & Luca Massaron & Claudio Nordio, 2019, "An Artificial Intelligence approach to Shadow Rating," Papers, arXiv.org, number 1912.09764, Dec.
- Kristina Bluwstein & Marcus Buckmann & Andreas Joseph & Miao Kang & Sujit Kapadia & Özgür Simsek, 2020, "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers, Bank of England, number 848, Jan.
- Boyue Fang & Yutong Feng, 2019, "Design of High-Frequency Trading Algorithm Based on Machine Learning," Papers, arXiv.org, number 1912.10343, Dec.
- Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020, "Economic policy uncertainty in the euro area: an unsupervised machine learning approach," Working Paper Series, European Central Bank, number 2359, Jan.
- Weipan Xu & Xiaozhen Qin & Xun Li & HaohuiCaron Chen & Morgan Frank & Alex Rutherford & Andrew Reeson & Iyad Rahwan, 2020, "China's First Workforce Skill Taxonomy," Papers, arXiv.org, number 2001.02863, Jan.
- Item repec:spo:wpmain:info:hdl:2441/52cps7rdns8iv8fr3f1kqm7iuv is not listed on IDEAS anymore
- Rickard Nyman & Paul Ormerod, 2020, "Text as Data: Real-time Measurement of Economic Welfare," Papers, arXiv.org, number 2001.03401, Jan.
- Onur Altindag & Stephen D. O’Connell & Aytug Sasmaz & Zeynep Balcioglu & Paola Cadoni & Matilda Jerneck & Aimee Kunze Foong, 2019, "Targeting Humanitarian Aid Using Administrative Data: Model Design And Validation," Working Papers, Economic Research Forum, number 1343, Sep, revised 20 Sep 2019.
- Léopold Simar & Valentin Zelenyuk, 2020, "Improving Finite Sample Approximation by Central Limit Theorems for Estimates from Data Envelopment Analysis," CEPA Working Papers Series, School of Economics, University of Queensland, Australia, number WP012020, Jan.
- Mark Kiermayer & Christian Wei{ss}, 2019, "Grouping of Contracts in Insurance using Neural Networks," Papers, arXiv.org, number 1912.09964, Dec.
- Jeffrey Ding & Allan Dafoe, 2020, "The Logic of Strategic Assets: From Oil to Artificial Intelligence," Papers, arXiv.org, number 2001.03246, Jan, revised May 2021.
- Shi, Chengchun & Fan, Ailin & Song, Rui & Lu, Wenbin, 2018, "High-dimensional A-learning for optimal dynamic treatment regimes," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 102113, Jun.