Report NEP-CMP-2023-03-27

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.

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Other reports in NEP-CMP

The following items were announced in this report:

• Nikhil Malik & Emaad Manzoor, 2023, "Does Machine Learning Amplify Pricing Errors in the Housing Market? -- The Economics of Machine Learning Feedback Loops," Papers, arXiv.org, number 2302.09438, Feb.
• Maudud Hassan Uzzal & Robert Ślepaczuk, 2023, "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-05.
• Sharma, Rahul, 2021, "The Effects of Artificial Intelligence on the World as a Whole from an Economic Perspective," MPRA Paper, University Library of Munich, Germany, number 116596, Apr.
• Ivan Guo & Nicolas Langren'e & Jiahao Wu, 2023, "Simultaneous upper and lower bounds of American-style option prices with hedging via neural networks," Papers, arXiv.org, number 2302.12439, Feb, revised Nov 2024.
• Martin Vesely, 2023, "Finding the Optimal Currency Composition of Foreign Exchange Reserves with a Quantum Computer," Working Papers, Czech National Bank, Research and Statistics Department, number 2023/1, Feb.
• Philippe Goulet Coulombe, 2021, "The Macroeconomy as a Random Forest," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 21-05, Jun.
• Lorena Torres Lahoz & Francisco Camara Pereira & Georges Sfeir & Ioanna Arkoudi & Mayara Moraes Monteiro & Carlos Lima Azevedo, 2023, "Attitudes and Latent Class Choice Models using Machine learning," Papers, arXiv.org, number 2302.09871, Feb.
• Arun Kumar Polala & Bernhard Hientzsch, 2023, "Parametric Differential Machine Learning for Pricing and Calibration," Papers, arXiv.org, number 2302.06682, Feb, revised Feb 2023.
• Deniz Preil & Michael Krapp, 2023, "Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation," Papers, arXiv.org, number 2302.07695, Feb.
• Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022, "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 22-04, Jul.
• Philippe Goulet Coulombe, 2022, "A Neural Phillips Curve and a Deep Output Gap," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 22-01, Jan.
• Shubhranshu Shekhar & Jetson Leder-Luis & Leman Akoglu, 2023, "Unsupervised Machine Learning for Explainable Health Care Fraud Detection," NBER Working Papers, National Bureau of Economic Research, Inc, number 30946, Feb.
• Alper Deniz Karakas, 2023, "Reevaluating the Taylor Rule with Machine Learning," Papers, arXiv.org, number 2302.08323, Feb.
• James Bell, 2023, "The global economic impact of AI technologies in the fight against financial crime," Papers, arXiv.org, number 2302.13823, Feb.
• Ganesh Iyer & T. Tony Ke, 2023, "Competitive Model Selection in Algorithmic Targeting," NBER Working Papers, National Bureau of Economic Research, Inc, number 31002, Mar.
• Daas, Piet & Hassink, Wolter & Klijs, Bart, 2023, "On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms," IZA Discussion Papers, Institute of Labor Economics (IZA), number 15941, Feb.
• Mckay Jensen & Nicholas Emery-Xu & Robert Trager, 2023, "Industrial Policy for Advanced AI: Compute Pricing and the Safety Tax," Papers, arXiv.org, number 2302.11436, Feb.
• Tom, Daniel, 2021, "Logistic Regression Collaborating with AI Beam Search," MPRA Paper, University Library of Munich, Germany, number 116592, Dec, revised 04 Mar 2023.
• Laura Nurski, 2023, "Artificial intelligence adoption in the public sector- a case study," Bruegel Working Papers, Bruegel, number node_8829, Mar.
• Philipp Adammer & Jan Pruser & Rainer Schussler, 2023, "Forecasting Macroeconomic Tail Risk in Real Time: Do Textual Data Add Value?," Papers, arXiv.org, number 2302.13999, Feb, revised May 2024.
• Vasilis Syrgkanis & Ruohan Zhan, 2023, "Post Reinforcement Learning Inference," Papers, arXiv.org, number 2302.08854, Feb, revised Oct 2025.

Report NEP-CMP-2023-03-27

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: