Report NEP-CMP-2024-07-15

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:

• Askitas, Nikos, 2024, "A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code," IZA Discussion Papers, Institute of Labor Economics (IZA), number 17014, May.
• Alexander Bakumenko & Katev{r}ina Hlav'av{c}kov'a-Schindler & Claudia Plant & Nina C. Hubig, 2024, "Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs," Papers, arXiv.org, number 2406.03614, Jun.
• Bivas Dinda, 2024, "Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy," Papers, arXiv.org, number 2406.02604, Jun.
• Mark E. Schaffer, 2023, "pystacked and ddml: Machine learning for prediction and causal inference in Stata," Economics Virtual Symposium 2023, Stata Users Group, number 04, Nov.
• Tohid Atashbar, 2024, "Reinforcement Learning from Experience Feedback: Application to Economic Policy," IMF Working Papers, International Monetary Fund, number 2024/114, Jun.
• Milen Arro-Cannarsa & Rolf Scheufele, 2024, "Nowcasting GDP: what are the gains from machine learning algorithms?," Working Papers, Swiss National Bank, number 2024-06.
• Francesco Audrino & Jonathan Chassot, 2024, "HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine Learning," Papers, arXiv.org, number 2406.08041, Jun.
• Buxmann, Peter & Hess, Thomas & Thatcher, Jason Bennett, 2024, "AI-Based Information Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 146094, Jun, DOI: 10.1007/s12599-020-00675-8.
• Sugarbayar Enkhbayar & Robert Ślepaczuk, 2024, "Predictive modeling of foreign exchange trading signals using machine learning techniques," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-10.
• Kumar, Pradeep & Nicodemo, Catia & Oreffice, Sonia & Quintana-Domeque, Climent, 2024, "Machine Learning and Multiple Abortions," IZA Discussion Papers, Institute of Labor Economics (IZA), number 17046, Jun.
• Adam Korniejczuk & Robert Ślepaczuk, 2024, "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-09.
• Seulki Chung, 2024, "Modelling and Forecasting Energy Market Volatility Using GARCH and Machine Learning Approach," Papers, arXiv.org, number 2405.19849, May.
• Ayush Singh & Anshu K. Jha & Amit N. Kumar, 2024, "Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation," Papers, arXiv.org, number 2405.12988, Apr.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2024, "Estimating Nonlinear Heterogeneous Agent Models with Neural Networks," The Warwick Economics Research Paper Series (TWERPS), University of Warwick, Department of Economics, number 1499.
• Junquera, Álvaro F. & Kern, Christoph, 2024, "From rules to forests: rule-based versus statistical models for jobseeker profiling," SocArXiv, Center for Open Science, number c7ps3, Jun, DOI: 10.31219/osf.io/c7ps3.
• Sven Goluv{z}a & Tomislav Kovav{c}evi'c & Tessa Bauman & Zvonko Kostanjv{c}ar, 2024, "Deep reinforcement learning with positional context for intraday trading," Papers, arXiv.org, number 2406.08013, Jun.
• Simon D Angus & Lachlan O'Neill, 2024, "Paired completion: quantifying issue-framing at scale with LLMs," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2024-02, Jun.
• Edward Sharkey & Philip Treleaven, 2024, "BERT vs GPT for financial engineering," Papers, arXiv.org, number 2405.12990, Apr.
• Bernhard Kasberger & Simon Martin & Hans-Theo Normann & Tobias Werner, 2024, "Algorithmic Cooperation," CESifo Working Paper Series, CESifo, number 11124.
• Fabrice Murtin & Max Salomon-Ermel, 2024, "Nowcasting subjective well-being with Google Trends: A meta-learning approach," OECD Papers on Well-being and Inequalities, OECD Publishing, number 27, Jun, DOI: 10.1787/cbdfb5d9-en.
• Emily Silcock & Abhishek Arora & Luca D'Amico-Wong & Melissa Dell, 2024, "Newswire: A Large-Scale Structured Database of a Century of Historical News," Papers, arXiv.org, number 2406.09490, Jun.
• Nicole Immorlica & Brendan Lucier & Aleksandrs Slivkins, 2024, "Generative AI as Economic Agents," Papers, arXiv.org, number 2406.00477, Jun.
• Raeid Saqur & Anastasis Kratsios & Florian Krach & Yannick Limmer & Jacob-Junqi Tian & John Willes & Blanka Horvath & Frank Rudzicz, 2024, "Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models," Papers, arXiv.org, number 2406.02969, Jun, revised Feb 2025.
• Nicolás Forteza & Elvira Prades & Marc Roca, 2024, "Analysing the VAT cut pass-through in Spain using web-scraped supermarket data and machine learning," Working Papers, Banco de España, number 2417, May, DOI: https://doi.org/10.53479/36652.
• Can Celebi & Stefan Penczynski, 2024, "Using Large Language Models for Text Classification in Experimental Economics," Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS), School of Economics, University of East Anglia, Norwich, UK., number 24-01, Jun.
• Daniel Vebman, 2024, "Quantifying the Reliance of Black-Box Decision-Makers on Variables of Interest," Papers, arXiv.org, number 2405.17225, May.
• Pablo Alvarez-Campana & Felix Villafanez & Fernando Acebes & David Poza, 2024, "Simulation-based approach for Multiproject Scheduling based on composite priority rules," Papers, arXiv.org, number 2406.02102, Jun.
• Joel Ong & Dorien Herremans, 2024, "DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts," Papers, arXiv.org, number 2406.08742, Jun.
• Guido Gazzani & Julien Guyon, 2024, "Pricing and calibration in the 4-factor path-dependent volatility model," Papers, arXiv.org, number 2406.02319, Jun, revised Feb 2025.
• Jingru Jia & Zehua Yuan & Junhao Pan & Paul E. McNamara & Deming Chen, 2024, "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context," Papers, arXiv.org, number 2406.05972, Jun, revised Oct 2024.
• Mengfei Chen & Mohamed Kharbeche & Mohamed Haouari & Weihong Grace Guo, 2024, "A simulation-optimization framework for food supply chain network design to ensure food accessibility under uncertainty," Papers, arXiv.org, number 2406.04439, Jun, revised Jun 2024.

Report NEP-CMP-2024-07-15

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: