Report NEP-CMP-2021-04-12

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:

• Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2021, "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," Papers, arXiv.org, number 2102.05405, Feb, revised Nov 2023.
• Shenhao Wang & Baichuan Mo & Yunhan Zheng & Stephane Hess & Jinhua Zhao, 2021, "Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark," Papers, arXiv.org, number 2102.01130, Feb, revised Mar 2025.
• Jorge Ignacio Gonz'alez C'azares & Aleksandar Mijatovi'c, 2021, "Monte Carlo algorithm for the extrema of tempered stable processes," Papers, arXiv.org, number 2103.15310, Mar, revised Dec 2022.
• Igor Halperin, 2021, "Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)," Papers, arXiv.org, number 2104.01040, Apr.
• Severin Reissl & Alessandro Caiani & Francesco Lamperti & Mattia Guerini & Fabio Vanni & Giorgio Fagiolo & Tommaso Ferraresi & Leonardo Ghezzi & Mauro Napoletano & Andrea Roventini, 2021, "Assessing the Economic Impact of Lockdowns in Italy: A Computational Input-Output Approach," GREDEG Working Papers, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, number 2021-15, Apr.
• Dawid Siwicki, 2021, "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-05.
• Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021, "Stock price forecast with deep learning," Papers, arXiv.org, number 2103.14081, Mar.
• Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021, "Forecasting with Deep Learning: S&P 500 index," Papers, arXiv.org, number 2103.14080, Mar.
• Pratyush Muthukumar & Jie Zhong, 2021, "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers, arXiv.org, number 2102.01290, Feb.
• Item repec:isu:genstf:202103290700001125 is not listed on IDEAS anymore

• Guillermo Gallego & Gerardo Berbeglia, 2021, "Bounds and Heuristics for Multi-Product Personalized Pricing," Papers, arXiv.org, number 2102.03038, Feb, revised Feb 2021.
• Mochammad Ridwan Ristyawan, 2021, "Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank," GATR Journals, Global Academy of Training and Research (GATR) Enterprise, number jfbr179, Mar, DOI: https://doi.org/10.35609/jfbr.2021..
• Jorino van Rhijn & Cornelis W. Oosterlee & Lech A. Grzelak & Shuaiqiang Liu, 2021, "Monte Carlo Simulation of SDEs using GANs," Papers, arXiv.org, number 2104.01437, Apr.
• Haohan Zhang, 2021, "A Comparative Evaluation of Predominant Deep Learning Quantified Stock Trading Strategies," Papers, arXiv.org, number 2103.15304, Mar, revised Apr 2021.
• Wenpin Tang & Xiao Xu & Xun Yu Zhou, 2021, "Asset Selection via Correlation Blockmodel Clustering," Papers, arXiv.org, number 2103.14506, Mar, revised Aug 2021.
• Rabab Haider & David D'Achiardi & Venkatesh Venkataramanan & Anurag Srivastava & Anjan Bose & Anuradha M. Annaswamy, 2021, "Reinventing the Utility for DERs: A Proposal for a DSO-Centric Retail Electricity Market," Papers, arXiv.org, number 2102.01269, Feb.
• Lazarus, Jessica & Bauer, Gordon PhD & Greenblatt, Jeffery PhD & Shaheen, Susan PhD, 2021, "Bridging the Income and Digital Divide with Shared Automated Electric Vehicles," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings, Institute of Transportation Studies, UC Berkeley, number qt5f1359rd, Mar.
• Yong Shi & Bo Li & Guangle Du, 2021, "Pyramid scheme in stock market: a kind of financial market simulation," Papers, arXiv.org, number 2102.02179, Feb, revised Feb 2021.
• Jonathan Berrisch & Florian Ziel, 2021, "CRPS Learning," Papers, arXiv.org, number 2102.00968, Feb, revised Nov 2021.
• Green, David A. & Kesselman, Jonathan Rhys & Tedds, Lindsay M. & Crisan, I. Daria & Petit, Gillian, 2020, "Basic Income Simulations for the Province of British Columbia," MPRA Paper, University Library of Munich, Germany, number 105918, Dec.
• Zhenhan Huang & Fumihide Tanaka, 2021, "MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management," Papers, arXiv.org, number 2102.03502, Feb, revised Feb 2022.
• Kevin Kuo & Ronald Richman, 2021, "Embeddings and Attention in Predictive Modeling," Papers, arXiv.org, number 2104.03545, Apr.
• Kunal Pattanayak & Vikram Krishnamurthy, 2021, "Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification," Papers, arXiv.org, number 2102.04594, Feb, revised Jul 2021.
• Nieddu, Marcello & Bertani, Filippo & Ponta, Linda, 2021, "Sustainability transition and digital trasformation: an agent-based perspective," MPRA Paper, University Library of Munich, Germany, number 106943, Apr.
• Samuel Cohen & Tanut Treetanthiploet, 2021, "Generalised correlated batched bandits via the ARC algorithm with application to dynamic pricing," Papers, arXiv.org, number 2102.04263, Feb, revised Oct 2022.
• Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021, "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers, arXiv.org, number 2102.04382, Feb.
• Livia Paranhos, 2021, "Predicting Inflation with Recurrent Neural Networks," Papers, arXiv.org, number 2104.03757, Apr, revised Oct 2023.
• Aymeric Vie, 2021, "Evolutionary Strategies with Analogy Partitions in p-guessing Games," Papers, arXiv.org, number 2103.14379, Mar.
• Zhong, Weifeng & Chan, Julian, 2020, "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers, George Mason University, Mercatus Center, number 10464, Feb.
• Aymeric Vie, 2021, "A Genetic Algorithm approach to Asymmetrical Blotto Games with Heterogeneous Valuations," Papers, arXiv.org, number 2103.14372, Mar.
• Ali Hirsa & Joerg Osterrieder & Branka Hadji Misheva & Wenxin Cao & Yiwen Fu & Hanze Sun & Kin Wai Wong, 2021, "The VIX index under scrutiny of machine learning techniques and neural networks," Papers, arXiv.org, number 2102.02119, Feb.
• Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021, "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science, number 2104, Apr.
• Mathias Reynaert & James M. Sallee, 2021, "Who Benefits When Firms Game Corrective Policies?," Post-Print, HAL, number hal-03167777, Feb, DOI: 10.1257/pol.20190019.
• Zbozinek, Tomislav Damir & Charpentier, Caroline Juliette & Qi, Song & mobbs, dean, 2021, "Ambiguous Outcome Magnitude in Economic Decision Making with Low and High Monetary Stakes," OSF Preprints, Center for Open Science, number 5q4g7, Apr, DOI: 10.31219/osf.io/5q4g7.
• Andreas Hornstein, 2021, "Quarantine, Contact Tracing, and Testing: Implications of an Augmented SEIR Model," Working Paper, Federal Reserve Bank of Richmond, number 21-08, Mar, DOI: 10.21144/wp21-08.
• Aguiar, Angel & Erwin Corong & Dominique van der Mensbrugghe, 2021, "Detailed Trade Policy Simulations Using a Global General Equilibrium Model," GTAP Working Papers, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, number 6199.
• Qixuan Luo & Yu Shi & Handong Li, 2021, "Research on Portfolio Liquidation Strategy under Discrete Times," Papers, arXiv.org, number 2103.15400, Mar.
• Zhong, Weifeng & Chan, Julian & Ho, Kwan-Yuet & Lee, Kit, 2020, "Words Speak Louder Than Numbers: Estimating China’s COVID Severity with Deep Learning," Working Papers, George Mason University, Mercatus Center, number 10955, Dec.
• Jaydip Sen & Sidra Mehtab, 2021, "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers, arXiv.org, number 2103.15096, Mar.
• Nicolas Curin & Michael Kettler & Xi Kleisinger-Yu & Vlatka Komaric & Thomas Krabichler & Josef Teichmann & Hanna Wutte, 2021, "A deep learning model for gas storage optimization," Papers, arXiv.org, number 2102.01980, Feb, revised Mar 2021.
• Tedds, Lindsay M. & Crisan, I. Daria, 2020, "Evaluating the Existing Basic Income Simulation Literature," MPRA Paper, University Library of Munich, Germany, number 105915, Dec.
• Nosier, Shereen & Beram, Reham & Mahrous, Mohamed, 2021, "Household Poverty in Egypt: Poverty Profile, Econometric Modeling and Policy Simulations," SocArXiv, Center for Open Science, number d8spt, Apr, DOI: 10.31219/osf.io/d8spt.
• Alina Malkova & Klara Sabirianova Peter & Jan Svejnar, 2021, "Labor Informality and Credit Market Accessibility," Papers, arXiv.org, number 2102.05803, Feb.
• Chepeliev, Maksym & Israel Osorio Rodarte & Dominique van der Mensbrugghe, 2021, "Distributional Impacts of Carbon Pricing Policies under Paris Agreement: Inter and Intra-Regional Perspectives," GTAP Working Papers, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, number 6194.
• Federico A. Bugni & Mengsi Gao, 2021, "Inference under Covariate-Adaptive Randomization with Imperfect Compliance," Papers, arXiv.org, number 2102.03937, Feb, revised Jul 2023.

Report NEP-CMP-2021-04-12

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: