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Report NEP-CMP-2022-01-03
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:
- Maximilien Germain & Huyên Pham & Xavier Warin, 2021, "Neural networks-based algorithms for stochastic control and PDEs in finance ," Post-Print, HAL, number hal-03115503, DOI: 10.1017/9781009028943.023.
- Christophe HURLIN & Christophe PERIGNON & Sébastien SAURIN, 2021, "The Fairness of Credit Scoring Models," LEO Working Papers / DR LEO, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans, number 2912.
- Thieltges, Andree, 2020, "Machine Learning Anwendungen in der betrieblichen Praxis: Praktische Empfehlungen zur betrieblichen Mitbestimmung," Mitbestimmungspraxis, Hans Böckler Foundation, Institute for Codetermination and Corporate Governance (I.M.U.), number 33.
- Yizhuo Li & Peng Zhou & Fangyi Li & Xiao Yang, 2021, "An Improved Reinforcement Learning Model Based on Sentiment Analysis," Papers, arXiv.org, number 2111.15354, Nov.
- Karim Barigou & Lukasz Delong, 2021, "Pricing equity-linked life insurance contracts with multiple risk factors by neural networks," Post-Print, HAL, number hal-02896141, Nov, DOI: 10.1016/j.cam.2021.113922.
- Erik Heilmann, 2021, "The impact of transparency policies on local flexibility markets in electrical distribution networks: A case study with artificial neural network forecasts," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202141.
- Knapp, S. & van de Velden, M., 2021, "Exploration of machine learning algorithms for maritime risk applications," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number 2021-03, Dec.
- Brunori, Paolo & Davillas, Apostolos & Jones, Andrew M. & Scarchilli, Giovanna, 2021, "Model-Based Recursive Partitioning to Estimate Unfair Health Inequalities in the United Kingdom Household Longitudinal Study," IZA Discussion Papers, Institute of Labor Economics (IZA), number 14925, Dec.
- Christophe Dutang & Quentin Guibert, 2021, "An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests," Post-Print, HAL, number hal-03448250, Nov, DOI: 10.1007/s11222-021-10059-x.
- Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio, 2021, "Structured Additive Regression and Tree Boosting," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-83, Sep.
- Fraunholz, Christoph & Miskiw, Kim K. & Kraft, Emil & Fichtner, Wolf & Weber, Christoph, 2021, "On the role of risk aversion and market design in capacity expansion planning," Working Paper Series in Production and Energy, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP), number 62, DOI: 10.5445/IR/1000140411.
- Mark Horridge, 2021, "Using CRETH to make quantities add up without efficiency bias," Centre of Policy Studies/IMPACT Centre Working Papers, Victoria University, Centre of Policy Studies/IMPACT Centre, number g-325, Dec.
- Item repec:hal:wpaper:hal-03436046 is not listed on IDEAS anymore
- Tomoaki Mikami & Hiroaki Yamagata & Jouchi Nakajima, 2021, "Using Text Analysis to Gauge the Reasons for Respondents' Assessment in the Economy Watchers Survey," Bank of Japan Research Laboratory Series, Bank of Japan, number 21-E-2, Dec.
- Gael M. Martin & David T. Frazier & Christian P. Robert, 2021, "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 24/21.