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. Stan Miles issued this report. It is usually issued weekly.

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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 2208.10896, arXiv.org, revised Mar 2023.
• Yang, Bill Huajian, 2022. "Modeling Path-Dependent State Transition by a Recurrent Neural Network," MPRA Paper 114188, University Library of Munich, Germany, revised 18 Jul 2022.
• Matthew Dicks & Andrew Paskaramoorthy & Tim Gebbie, 2022. "A simple learning agent interacting with an agent-based market model," Papers 2208.10434, arXiv.org, 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 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
• 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 4030, Stanford University, Graduate School of Business.
• Po-Yi Liu & Chi-Hua Wang & Henghsiu Tsai, 2022. "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing," Papers 2208.09372, arXiv.org, revised Sep 2022.
• Boyi Jin, 2022. "An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm," Papers 2208.10707, arXiv.org, revised Aug 2022.
• Yuchao Dong, 2022. "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers 2208.02409, arXiv.org, revised Sep 2023.
• Ivonne Schwartz & Mark Kirstein, 2022. "Time is limited on the road to asymptopia," Papers 2208.08169, arXiv.org.
• Daphne Cornelisse & Thomas Rood & Mateusz Malinowski & Yoram Bachrach & Tal Kachman, 2022. "Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members," Papers 2208.08798, arXiv.org.
• Denizalp Goktas & Amy Greenwald, 2022. "Gradient Descent Ascent in Min-Max Stackelberg Games," Papers 2208.09690, arXiv.org.
• 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 115770, London School of Economics and Political Science, LSE Library.
• Xia Han & Ruodu Wang & Xun Yu Zhou, 2022. "Choquet regularization for reinforcement learning," Papers 2208.08497, arXiv.org.
• Zhongze Cai & Hanzhao Wang & Kalyan Talluri & Xiaocheng Li, 2022. "Deep Learning for Choice Modeling," Papers 2208.09325, arXiv.org.
• Ali Saeb, 2022. "Stock Prices as Janardan Galton Watson Process," Papers 2208.08496, arXiv.org.