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Report NEP-CMP-2022-09-19
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:
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2022, "pystacked: Stacking generalization and machine learning in Stata," Papers, arXiv.org, number 2208.10896, Aug, revised Mar 2023.
- Yang, Bill Huajian, 2022, "Modeling Path-Dependent State Transition by a Recurrent Neural Network," MPRA Paper, University Library of Munich, Germany, number 114188, Aug, revised 18 Jul 2022.
- Matthew Dicks & Andrew Paskaramoorthy & Tim Gebbie, 2022, "A simple learning agent interacting with an agent-based market model," Papers, arXiv.org, number 2208.10434, Aug, revised Nov 2023.
- Daniel Stempel & Johannes Zahner, 2022, "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202232.
- Angelopoulos, Anastasios N. & Bates, Stephen & Candes, Emmanuel J. & Jordan, Michael I. & Lei, Lihua, 2022, "Learn Then Test: Calibrating Predictive Algorithms to Achieve Risk Control," Research Papers, Stanford University, Graduate School of Business, number 4030, Apr.
- Po-Yi Liu & Chi-Hua Wang & Henghsiu Tsai, 2022, "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing," Papers, arXiv.org, number 2208.09372, Aug, revised Sep 2022.
- Boyi Jin, 2022, "An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm," Papers, arXiv.org, number 2208.10707, Aug, revised Aug 2022.
- Yuchao Dong, 2022, "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers, arXiv.org, number 2208.02409, Aug, revised Sep 2023.
- Ivonne Schwartz & Mark Kirstein, 2022, "Time is limited on the road to asymptopia," Papers, arXiv.org, number 2208.08169, Aug.
- Daphne Cornelisse & Thomas Rood & Mateusz Malinowski & Yoram Bachrach & Tal Kachman, 2022, "Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members," Papers, arXiv.org, number 2208.08798, Aug.
- Denizalp Goktas & Amy Greenwald, 2022, "Gradient Descent Ascent in Min-Max Stackelberg Games," Papers, arXiv.org, number 2208.09690, Aug.
- Paz, Hellen & Maia, Mateus & Moraes, Fernando & Lustosa, Ricardo & Costa, Lilia & Macêdo, Samuel & Barreto, Marcos E. & Ara, Anderson, 2020, "Local processing of massive databases with R: a national analysis of a Brazilian social programme," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 115770, Dec.
- Xia Han & Ruodu Wang & Xun Yu Zhou, 2022, "Choquet regularization for reinforcement learning," Papers, arXiv.org, number 2208.08497, Aug.
- Zhongze Cai & Hanzhao Wang & Kalyan Talluri & Xiaocheng Li, 2022, "Deep Learning for Choice Modeling," Papers, arXiv.org, number 2208.09325, Aug.
- Ali Saeb, 2022, "Stock Prices as Janardan Galton Watson Process," Papers, arXiv.org, number 2208.08496, Aug.