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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.
- 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, 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.