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Report NEP-CMP-2022-07-18
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:
- Zachary Feinstein & Birgit Rudloff, 2022, "Deep Learning the Efficient Frontier of Convex Vector Optimization Problems," Papers, arXiv.org, number 2205.07077, May, revised May 2024.
- Krikamol Muandet, 2022, "(Im)possibility of Collective Intelligence," Papers, arXiv.org, number 2206.02786, Jun, revised May 2025.
- Vitezslav Titl & Fritz Schiltz, 2021, "Identifying Politically Connected Firms: A Machine Learning Approach," Working Papers, Utrecht School of Economics, number 2110.
- Item repec:hal:wpspec:halshs-03231786 is not listed on IDEAS anymore
- Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022, "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers, arXiv.org, number 2206.01825, Jun, revised Nov 2022.
- Lucrezia Fanti & Marcelo C. Pereira & Maria Enrica Virgillito, 2022, "The North-South divide: sources of divergence, policies for convergence," DISCE - Working Papers del Dipartimento di Politica Economica, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE), number dipe0027, May.
- Laurence Barry & Arthur Charpentier, 2022, "The Fairness of Machine Learning in Insurance: New Rags for an Old Man?," Papers, arXiv.org, number 2205.08112, May.
- Juan Carlos Escanciano & Joel Robert Terschuur, 2022, "Debiased Machine Learning U-statistics," Papers, arXiv.org, number 2206.05235, Jun, revised Oct 2025.
- Ekaterina Oparina & Caspar Kaiser & Niccol`o Gentile & Alexandre Tkatchenko & Andrew E. Clark & Jan-Emmanuel De Neve & Conchita D'Ambrosio, 2022, "Human Wellbeing and Machine Learning," Papers, arXiv.org, number 2206.00574, Jun.
- Koen W. de Bock & Arno de Caigny, 2021, "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print, HAL, number hal-03391564, Nov, DOI: 10.1016/j.dss.2021.113523.
- John Gilbert & Onur A. Koska & Reza Oladi, 2022, "Building and Using Nonlinear Excel Simulations: An Application to the Specific Factors Model," Working Papers in Economics, University of Canterbury, Department of Economics and Finance, number 22/08, Mar.
- Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022, ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202210, Jul, revised Jul 2022.
- Dirk Czarnitzki & Gastón P Fernández & Christian Rammer, 2022, "Artificial Intelligence and Firm-level Productivity," Working Papers of Department of Management, Strategy and Innovation, Leuven, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven, number 690486, Feb.
- Rohith Mahadevan & Sam Richard & Kishore Harshan Kumar & Jeevitha Murugan & Santhosh Kannan & Saaisri & Tarun & Raja CSP Raman, 2022, "Payday loans -- blessing or growth suppressor? Machine Learning Analysis," Papers, arXiv.org, number 2205.15320, May.
- Leonid Kogan & Indrajit Mitra, 2022, "Near-Rational Equilibria in Heterogeneous-Agent Models: A Verification Method," NBER Working Papers, National Bureau of Economic Research, Inc, number 30111, Jun.