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Report NEP-CMP-2020-11-02
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:
- Mellacher, Patrick, 2020, "COVID-Town: An Integrated Economic-Epidemiological Agent-Based Model," MPRA Paper, University Library of Munich, Germany, number 103661, Oct.
- João Amador & Tiago Alves, 2020, "Assessing the Scoreboard of the EU Macroeconomic Imbalances Procedure: (Machine) Learning from Decisions," Working Papers, Banco de Portugal, Economics and Research Department, number w202016.
- Harmenberg, Karl, 2020, "Aggregating Heterogeneous-Agent Models with Permanent Income Shocks," Working Papers, Copenhagen Business School, Department of Economics, number 13-2020, Sep.
- Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020, "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv, Center for Open Science, number 9vdwf, Oct, DOI: 10.31219/osf.io/9vdwf.
- Martin Spielauer & Thomas Horvath & Marian Fink & Gemma Abio & Guadalupe Souto Nieves & Concepció Patxot & Tanja Istenič, 2020, "microWELT: Microsimulation Projection of Indicators of the Economic Effects of Population Ageing Based on Disaggregated National Transfer Accounts," WIFO Working Papers, WIFO, number 612, Oct.
- Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay & Jamal Atif, 2020, "AAMDRL: Augmented Asset Management with Deep Reinforcement Learning," Papers, arXiv.org, number 2010.08497, Sep.
- Martin Spielauer & Thomas Horvath & Walter Hyll & Marian Fink, 2020, "microWELT: Socio-Demographic Parameters and Projections for Austria, Spain, Finland, and the UK," WIFO Working Papers, WIFO, number 611, Oct.
- Roland Hodler & Michael Lechner & Paul A. Raschky, 2020, "Reassessing the Resource Curse using Causal Machine Learning," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2020-01, Sep.