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Report NEP-CMP-2023-05-22
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:
- Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023, "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers, arXiv.org, number 2304.12501, Apr, revised Feb 2024.
- Raj G. Patel & Tomas Dominguez & Mohammad Dib & Samuel Palmer & Andrea Cadarso & Fernando De Lope Contreras & Abdelkader Ratnani & Francisco Gomez Casanova & Senaida Hern'andez-Santana & 'Alvaro D'iaz, 2023, "Application of Tensor Neural Networks to Pricing Bermudan Swaptions," Papers, arXiv.org, number 2304.09750, Apr, revised Mar 2024.
- Li Tang & Chuanli Tang & Qi Fu, 2023, "Enhanced multilayer perceptron with feature selection and grid search for travel mode choice prediction," Papers, arXiv.org, number 2304.12698, Apr, revised Oct 2023.
- Ajit Desai, 2023, "Machine Learning for Economics Research: When What and How?," Papers, arXiv.org, number 2304.00086, Mar, revised Apr 2023.
- Mayank Ratan Bhardwaj & Jaydeep Pawar & Abhijnya Bhat & Deepanshu & Inavamsi Enaganti & Kartik Sagar & Y. Narahari, 2023, "An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction," Papers, arXiv.org, number 2304.09761, Apr.
- Ryuichiro Hashimoto & Kakeru Miura & Yasunori Yoshizaki, 2023, "Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning," Bank of Japan Working Paper Series, Bank of Japan, number 23-E-6, Apr.
- Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2025, "Deep parametric portfolio policies," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 23-01, revised 2025.
- Kyungsub Lee, 2023, "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Papers, arXiv.org, number 2304.11883, Apr.
- Hannes Wallimann & Silvio Sticher, 2023, "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers, arXiv.org, number 2304.11888, Apr.
- Prest, Brian C. & Wichman, Casey & Palmer, Karen, 2021, "RCTs Against the Machine: Can Machine Learning Prediction Methods Recover Experimental Treatment Effects?," RFF Working Paper Series, Resources for the Future, number 21-30, Sep.
- Ginevra Buratti & Alessio D'Ignazio, 2023, "Improving the effectiveness of financial education programs. A targeting approach," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 765, Apr.
- Csaba Burger & Mihály Berndt, 2023, "Error Spotting with Gradient Boosting: A Machine Learning-Based Application for Central Bank Data Quality," MNB Occasional Papers, Magyar Nemzeti Bank (Central Bank of Hungary), number 2023/148.
- Martina Jakob & Sebastian Heinrich, 2023, "Measuring Human Capital with Social Media Data and Machine Learning," University of Bern Social Sciences Working Papers, University of Bern, Department of Social Sciences, number 46, May, DOI: 10.48350/182366.
- Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022, "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers, Federal Reserve Bank of Philadelphia, number 22-39, Nov, DOI: 10.21799/frbp.wp.2022.39.
- Müller, Henrik & Schmidt, Tobias & Rieger, Jonas & Hornig, Nico & Hufnagel, Lena Marie, 2023, "The inflation attention cycle: Updating the Inflation Perception Indicator (IPI) up to February 2023. A research note," DoCMA Working Papers, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA), number 13, DOI: 10.17877/DE290R-23141.
- Athey, Susan & Karlan, Dean & Palikot, Emil & Yuan, Yuan, 2022, "Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces," Research Papers, Stanford University, Graduate School of Business, number 4071, Nov.
- Sylvain Barthélémy & Fabien Rondeau & Virginie Gautier, 2023, "Early Warning System for Currency Crises using Long Short-Term Memory and Gated Recurrent Unit Neural Networks," Economics Working Paper Archive (University of Rennes & University of Caen), Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS, number 2023-05, Apr.
- Marco Amendola & Francesco Lamperti & Andrea Roventini & Alessandro Sapio, 2023, "Energy efficiency policies in an agent-based macroeconomic model," LEM Papers Series, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy, number 2023/20, May.
- Jiwook Kim & Minhyeok Lee, 2023, "Portfolio Optimization using Predictive Auxiliary Classifier Generative Adversarial Networks with Measuring Uncertainty," Papers, arXiv.org, number 2304.11856, Apr.
- Breen, Casey & Seltzer, Nathan, 2023, "The Unpredictability of Individual-Level Longevity," SocArXiv, Center for Open Science, number znsqg, Apr, DOI: 10.31219/osf.io/znsqg.
- John J. Horton & Apostolos Filippas & Benjamin S. Manning, 2023, "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers, National Bureau of Economic Research, Inc, number 31122, Apr.
- Kasy, Maximilian, 2023, "The Political Economy of AI: Towards Democratic Control of the Means of Prediction," SocArXiv, Center for Open Science, number x7pcy, Apr, DOI: 10.31219/osf.io/x7pcy.
- Simon Briole & Augustin Colette & Emmanuelle Lavaine, 2023, "The Heterogeneous Effects of Lockdown Policies on Air Pollution," Working Papers, HAL, number hal-04084912, Apr.