Report NEP-CMP-2020-04-06

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

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Other reports in NEP-CMP

The following items were announced in this report:

• Johannes Dahlke & Kristina Bogner & Matthias Mueller & Thomas Berger & Andreas Pyka & Bernd Ebersberger, 2020, "Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)," Papers, arXiv.org, number 2003.11985, Mar.
• Rodier, Caroline & Jaller, Miguel & Pourrahmani, Elham & Pahwa, Anmol & Bischoff, Joschka & Freedman, Joel, 2020, "Automated Vehicles are Expected to Increase Driving and Emissions Without Policy Intervention," Institute of Transportation Studies, Working Paper Series, Institute of Transportation Studies, UC Davis, number qt4sf2n6rs, Mar.
• Yang Chen & Emerson Li, 2020, "EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm," Papers, arXiv.org, number 2003.11473, Mar.
• J. Raimbault & J. Broere & M. Somveille & J. M. Serna & E. Strombom & C. Moore & B. Zhu & L. Sugar, 2020, "A spatial agent based model for simulating and optimizing networked eco-industrial systems," Papers, arXiv.org, number 2003.14133, Mar.
• Steyn, Dimitri H. W. & Greyling, Talita & Rossouw, Stephanie & Mwamba, John M., 2020, "Sentiment, emotions and stock market predictability in developed and emerging markets," GLO Discussion Paper Series, Global Labor Organization (GLO), number 502.
• Kohei Maehashi & Mototsugu Shintani, 2020, "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo, number CIRJE-F-1146, Mar.
• Paolo Massaro & Ilaria Vannini & Oliver Giudice, 2020, "Institutional sector classifier, a machine learning approach," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 548, Mar.
• Tyrel Stokes & Russell Steele & Ian Shrier, 2020, "Causal Simulation Experiments: Lessons from Bias Amplification," Papers, arXiv.org, number 2003.08449, Mar.
• Nicola Curci & Pietro Rizza & Marzia Romanelli & Marco Savegnago, 2020, "Irpef: (Un)Fairness and (in)efficiency. A structural analysis based on the BIMic microsimulation model," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 546, Mar.
• Frédéric Abergel & Côme Huré & Huyên Pham, 2020, "Algorithmic trading in a microstructural limit order book model," Post-Print, HAL, number hal-01514987, Aug, DOI: 10.1080/14697688.2020.1729396.
• Giuseppe Attanasi & Samuele Centorrino & Elena Manzoni, 2020, "Zero-Intelligence vs. Human Agents: An Experimental Analysis of the Efficiency of Double Auctions and Over-the-Counter Markets of Varying Sizes," GREDEG Working Papers, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, number 2020-10, Mar.
• Krebs, Oliver, 2020, "RIOTs in Germany - constructing an interregional input-output table for Germany," University of Tübingen Working Papers in Business and Economics, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics, number 132, DOI: 10.15496/publikation-40407.
• Debnath, Ramit & darby, Sarah & Bardhan, Ronita & Mohaddes, Kamiar & Sunikka-Blank, Minna, 2020, "A nested computational social science approach for deep-narrative analysis in energy policy research," SocArXiv, Center for Open Science, number hvcb5, Mar, DOI: 10.31219/osf.io/hvcb5.
• Cerulli, Giovanni, 2020, "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper, University Library of Munich, Germany, number 99111, Mar.
• Flor, Nick V., 2020, "Research Notes: Data Structures for Social Media Machine Learning — The Tweet Term Matrix (TTM) and Tweet Bio-Term Matrix (TBTM)," SocArXiv, Center for Open Science, number tp5mu, Mar, DOI: 10.31219/osf.io/tp5mu.
• Enkhbayar Shagdar & Tomoyoshi Nakajima, 2018, "Economic Effects of the USA - China Trade War: CGE Analysis with the GTAP 9.0a Data Base," Discussion papers, ERINA - Economic Research Institute for Northeast Asia, number 1806e, Dec.
• Fabio Zambuto & Maria Rosaria Buzzi & Giuseppe Costanzo & Marco Di Lucido & Barbara La Ganga & Pasquale Maddaloni & Fabio Papale & Emiliano Svezia, 2020, "Quality checks on granular banking data: an experimental approach based on machine learning?," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 547, Mar.
• Siddiqui, Rizwana & Kemal, A.R., 2019, "Poverty-reducing or Poverty-inducing? A CGE-based Analysis of Foreign Capital Inflows in Pakistan," MPRA Paper, University Library of Munich, Germany, number 99013, Jan, revised Jul 2019.
• Xinyi Guo & Jinfeng Li, 2020, "A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency," Papers, arXiv.org, number 2003.08137, Mar, revised Apr 2020.
• Jesus Fernandez-Villaverde, 2020, "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 20-010, Mar.
• Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021, "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers, HAL, number hal-02507499, Jan.
• Jianbin Lin & Zhiqiang Zhang & Jun Zhou & Xiaolong Li & Jingli Fang & Yanming Fang & Quan Yu & Yuan Qi, 2020, "NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay," Papers, arXiv.org, number 2004.00201, Mar.
• Víctor Morales-Oñate & Federico Crudu & Moreno Bevilacqua, 2020, "Blockwise Euclidean likelihood for spatio-temporal covariance models," Department of Economics University of Siena, Department of Economics, University of Siena, number 822, Mar.
• Nicola Cufaro Petroni & Piergiacomo Sabino, 2020, "Gamma Related Ornstein-Uhlenbeck Processes and their Simulation," Papers, arXiv.org, number 2003.08810, Mar.
• Francesco Bloise & Paolo Brunori & Patrizio Piraino, 2020, "Estimating intergenerational income mobility on sub-optimal data: a machine learning approach," Working Papers, ECINEQ, Society for the Study of Economic Inequality, number 526, Mar.
• Mahdi Ben Jelloul & Antoine Bozio & Sophie Cottet & Brice Fabre & Claire Leroy, 2018, "Revenu de base – Simulations en vue d’une expérimentation," PSE Working Papers, HAL, number halshs-02514725, Jun.

Report NEP-CMP-2020-04-06

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

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

Other reports in NEP-CMP

The following items were announced in this report: