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Report NEP-BIG-2021-05-10
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:
- Gambacorta, Leonardo & Amstad, Marlene & He, Chao & XIA, Fan Dora, 2021, "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15682, Jan.
- Qingfeng Liu & Yang Feng, 2021, "Machine Collaboration," Papers, arXiv.org, number 2105.02569, May, revised Feb 2024.
- Martin Huber & Jonas Meier & Hannes Wallimann, 2021, "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Papers, arXiv.org, number 2105.01426, May, revised Jun 2022.
- David Imhof & Hannes Wallimann, 2021, "Detecting bid-rigging coalitions in different countries and auction formats," Papers, arXiv.org, number 2105.00337, May.
- Nekoei, Arash & Sinn, Fabian, 2021, "Human Biographical Record (HBR)," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15825, Feb.
- Riccardo Aiolfi & Nicola Moreni & Marco Bianchetti & Marco Scaringi & Filippo Fogliani, 2021, "Learning Bermudans," Papers, arXiv.org, number 2105.00655, May.
- Yusuke Narita & Kohei Yata, 2021, "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2283, Apr.
- Wunsch, Conny & Strittmatter, Anthony, 2021, "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15840, Feb.
- Navid Mottaghi & Sara Farhangdoost, 2021, "Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting," Papers, arXiv.org, number 2105.02785, May.
- Yusuke Narita & Kohei Yata, 2021, "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Working Papers, Human Capital and Economic Opportunity Working Group, number 2021-022, Apr.
- Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021, "Automatic Debiased Machine Learning via Riesz Regression," Papers, arXiv.org, number 2104.14737, Apr, revised Mar 2024.
- Rovetta, Alessandro, 2021, "The Impact of COVID-19 on Conspiracy Attitudes and Risk Perception in Italy: an Infodemiological Survey through Google Trends," SocArXiv, Center for Open Science, number 83f9g, Apr, DOI: 10.31219/osf.io/83f9g.
- Gorodnichenko, Yuriy & Maliar, Serguei & Naubert, Christopher, 2020, "Household Savings and Monetary Policy under Individual and Aggregate Stochastic Volatility," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15614, Dec.
- Wilson, Thomas & Grossman, Irina & Alexander, Monica & Rees, Philip & Temple, Jeromey, 2021, "Methods for small area population forecasts: state-of-the-art and research needs," SocArXiv, Center for Open Science, number sp6me, Apr, DOI: 10.31219/osf.io/sp6me.
- Jayachandran, Seema & Biradavolu, Monica & Cooper, Jan, 2021, "Using machine learning and qualitative interviews to design a five-question women's agency index," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15961, Mar.
- Marcellino, Massimiliano & Stevanovic, Dalibor & Goulet Coulombe, Philippe, 2021, "Can Machine Learning Catch the COVID-19 Recession?," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15867, Mar.
- Qiutong Guo & Shun Lei & Qing Ye & Zhiyang Fang, 2021, "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price," Papers, arXiv.org, number 2105.00707, May.
- Kulkarni, Shruti, 2020, "Using Machine Learning to Analyze Climate Change Technology Transfer (CCTT)," SocArXiv, Center for Open Science, number zyb3j, Apr, DOI: 10.31219/osf.io/zyb3j.
- Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021, "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers, Department of Economics, Johannes Kepler University Linz, Austria, number 2021-08, Apr.
- Montserrat Lopez-Cobo & Riccardo Righi & Sofia Samoili & Miguel Vazquez-Prada Baillet & Melisande Cardona & Giuditta De-Prato, 2021, "AI Watch Index. Policy relevant dimensions to assess Europe’s performance in artificial intelligence," JRC Research Reports, Joint Research Centre, number JRC124424, Apr.
- Wehrheim, Lino & Jopp, Tobias Alexander & Spoerer, Mark, 2021, "Turn, turn, turn: A digital history of German historiography, 1950-2019," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 31, DOI: 10.18452/22795.
- Gorodnichenko, Yuriy & Pham, Tho & Talavera, Oleksandr, 2021, "The Voice of Monetary Policy," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15932, Mar.
- Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021, "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15854, Feb.
- Korinek, Anton & Stiglitz, Joseph, 2021, "Artificial Intelligence, Globalization, and Strategies for Economic Development," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15772, Feb.