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Report NEP-CMP-2021-10-11
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:
- Amine Assouel & Antoine Jacquier & Alexei Kondratyev, 2021, "A Quantum Generative Adversarial Network for distributions," Papers, arXiv.org, number 2110.02742, Oct.
- K. S. Naik, 2021, "Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach," Papers, arXiv.org, number 2110.02206, Oct.
- Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021, "RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests," Papers, arXiv.org, number 2110.03031, Oct, revised Jun 2022.
- Rui & Shi, 2021, "Can an AI agent hit a moving target?," Papers, arXiv.org, number 2110.02474, Oct, revised Oct 2022.
- Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021, "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers, arXiv.org, number 2110.01325, Oct, revised Oct 2021.
- Steven DiSilvio & Yu & Luo & Anthony Ozerov, 2021, "Traders in a Strange Land: Agent-based discrete-event market simulation of the Figgie card game," Papers, arXiv.org, number 2110.00879, Oct.
- Steven Campbell & Yichao Chen & Arvind Shrivats & Sebastian Jaimungal, 2021, "Deep Learning for Principal-Agent Mean Field Games," Papers, arXiv.org, number 2110.01127, Oct.
- Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021, "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series, School of Economics, University of Queensland, Australia, number WP082021, Jun.
- Stephanie Assad & Emilio Calvano & Giacomo Calzolari & Robert Clark & Vincenzo Denicolo & Daniel Ershov & Justin Pappas Johnson & Sergio Pastorello & Andrew Rhodes & Lei Xu & Matthijs Wildenbeest, 2021, "Autonomous algorithmic collusion: Economic research and policy implications," Post-Print, HAL, number hal-03360129, Sep, DOI: 10.1093/oxrep/grab011.
- Athanasia Dimitriadou & Anna Agrapetidou & Periklis Gogas & Theophilos Papadimitriou, 2021, "Credit Rating Agencies: Evolution or Extinction?," DUTH Research Papers in Economics, Democritus University of Thrace, Department of Economics, number 9-2021, Oct.
- Manuel Arellano & Stéphane Bonhomme & Micole De Vera & Laura Hospido & Siqi Wei, 2021, "Income Risk Inequality: Evidence from Spanish Administrative Records," Working Papers, CEMFI, number wp2021_2109, Sep.
- Felipe Nazare & Alexandre Street, 2021, "Solving Multistage Stochastic Linear Programming via Regularized Linear Decision Rules: An Application to Hydrothermal Dispatch Planning," Papers, arXiv.org, number 2110.03146, Oct, revised Jan 2023.
- Item repec:hit:hmicwp:246 is not listed on IDEAS anymore
- Balie, Jean & Valera, Harold Glenn A. & Narayanan Gopalakrishnan, Badri & Pede, Valerien O., 2021, "The impacts of reforming agricultural policy support on cereal prices: A CGE modeling approach," 2021 Annual Meeting, August 1-3, Austin, Texas, Agricultural and Applied Economics Association, number 313939, Aug, DOI: 10.22004/ag.econ.313939.
- Alessandro Dalla Benetta & Maciej Sobolewski & Daniel Nepelski, 2021, "AI Watch: 2020 EU AI investments," JRC Research Reports, Joint Research Centre, number JRC126477, Sep.
- Lochner, Benjamin, 2019, "A simple algorithm to link "last hires" from the Job Vacancy Survey to administrative records," FDZ-Methodenreport, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], number 201906 (en), Dec, DOI: 10.5164/IAB.FDZM.1906.en.v1.