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Report NEP-CMP-2024-01-01
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:
- Parth Daxesh Modi & Kamyar Arshi & Pertami J. Kunz & Abdelhak M. Zoubir, 2023, "A Data-driven Deep Learning Approach for Bitcoin Price Forecasting," Papers, arXiv.org, number 2311.06280, Oct.
- Stempel, Daniel & Zahner, Johannes, 2023, "Whose Inflation Rates Matter Most? A DSGE Model and Machine Learning Approach to Monetary Policy in the Euro Area," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277627.
- Khaled AlAjmi & Jose Deodoro & Mr. Ashraf Khan & Kei Moriya, 2023, "Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database," IMF Working Papers, International Monetary Fund, number 2023/241, Nov.
- Holtemöller, Oliver & Kozyrev, Boris, 2023, "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277688.
- Ksenia E. Chistyakova & Tatiana B. Kazakova, 2023, "Grammar In Language Models: Bert Study," HSE Working papers, National Research University Higher School of Economics, number WP BRP 115/LNG/2023.
- Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023, "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 21/23.
- Yuxi Heluo & Kexin Wang & Charles W. Robson, 2023, "Do we listen to what we are told? An empirical study on human behaviour during the COVID-19 pandemic: neural networks vs. regression analysis," Papers, arXiv.org, number 2311.13046, Nov.
- Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2023, "Sovereign Risk and Economic Complexity: Machine Learning Insights on Causality and Prediction," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202315, Nov, revised Nov 2023.
- Antoine Jacquier & Oleksiy Kondratyev & Gordon Lee & Mugad Oumgari, 2023, "Quantum Computing for Financial Mathematics," Papers, arXiv.org, number 2311.06621, Nov.
- Thomas Chalaux & David Turner, 2023, "Doombot: a machine learning algorithm for predicting downturns in OECD countries," OECD Economics Department Working Papers, OECD Publishing, number 1780, Dec, DOI: 10.1787/4ed7acc3-en.
- Osman, Adam & Speer, Jamin D., 2023, "Stigma and Take-up of Labor Market Assistance: Evidence from Two Field Experiments," IZA Discussion Papers, Institute of Labor Economics (IZA), number 16599, Nov.