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Report NEP-CMP-2023-12-04
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:
- Ryan Chipwanya, 2023, "Stock Market Directional Bias Prediction Using ML Algorithms," Papers, arXiv.org, number 2310.16855, Oct.
- Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023, "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 23-04, Nov, revised Nov 2023.
- Sangkyu Lee, 2023, "Strategies for Optimizing Policy Outcomes through Machine Learning: A Case Study on Korean R&D Project Assessment," Industrial Economic Review, Korea Institute for Industrial Economics and Trade, number 23-22, Oct.
- Nabeel, Rao, 2023, "Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information," OSF Preprints, Center for Open Science, number 82pqr, Jan, DOI: 10.31219/osf.io/82pqr.
- Xiong Xiong & Fan Yang & Li Su, 2023, "Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques," Papers, arXiv.org, number 2310.19200, Oct.
- Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2023, "Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/23/01.
- Gebreel, Alia Youssef, 2023, "Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans Click to," OSF Preprints, Center for Open Science, number 6we4m, Jan, DOI: 10.31219/osf.io/6we4m.
- Marco Delogu & Raffaelle Lagravinese & Dimitri Paolini & Giuliano Resce, 2020, "Predicting dropout from higher education: Evidence from Italy," DEM Discussion Paper Series, Department of Economics at the University of Luxembourg, number 22-06.
- Ola, Aranuwa Felix, 2023, "Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans Click to," OSF Preprints, Center for Open Science, number u7rpv, Jan, DOI: 10.31219/osf.io/u7rpv.
- Nian Si, 2023, "Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach," Papers, arXiv.org, number 2310.17496, Oct, revised Apr 2024.
- Kang Gao & Stephen Weston & Perukrishnen Vytelingum & Namid R. Stillman & Wayne Luk & Ce Guo, 2023, "Deeper Hedging: A New Agent-based Model for Effective Deep Hedging," Papers, arXiv.org, number 2310.18755, Oct.
- Mariam Dundua & Otar Gorgodze, 2022, "Application of Artificial Intelligence for Monetary Policy-Making," NBG Working Papers, National Bank of Georgia, number 02/2022, Nov.
- Mignot, Sarah & Westerhoff, Frank H., 2023, "Explaining the stylized facts of foreign exchange markets with a simple agent-based version of Paul de Grauwe's chaotic exchange rate model," BERG Working Paper Series, Bamberg University, Bamberg Economic Research Group, number 189.
- Hendrik Jenett, 2023, "Composition of Real Estate Values: Analyzing Time-Varying Credit and Market Data Using Neural Networks," ERES, European Real Estate Society (ERES), number eres2023_183, Jan.
- Bastian Krämer & Moritz Stang & Vanja Doskoc & Wolfgang Schäfers & Friedrich Tobias, 2023, "Automated Valuation Models: Improving Model Performance by Choosing the Optimal Spatial Training Level," ERES, European Real Estate Society (ERES), number eres2023_120, Jan.
- Marcelo DEL Cajias & Anna Freudenreich, 2023, "What are tenants demanding the most? A machine learning approach for the prediction of time on market," ERES, European Real Estate Society (ERES), number eres2023_35, Jan.
- Seulki Chung, 2023, "Inside the black box: Neural network-based real-time prediction of US recessions," Papers, arXiv.org, number 2310.17571, Oct, revised May 2024.
- Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023, "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers, arXiv.org, number 2311.02789, Nov, revised Apr 2024.
- Leonardo Ciambezi & Mattia Guerini & Mauro Napoletano & Andrea Roventini, 2023, "Accounting for the Multiple Sources of Inflation: an Agent-Based Model Investigation," GREDEG Working Papers, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, number 2023-14, Aug, revised Jun 2024.
- Goel, Rajeev K. & Nelson, Michael A., 2023, "Awareness of artificial intelligence: Diffusion of information about AI versus ChatGPT in the United States," Kiel Working Papers, Kiel Institute for the World Economy, number 2259.
- Charles I. Jones, 2023, "The A.I. Dilemma: Growth versus Existential Risk," NBER Working Papers, National Bureau of Economic Research, Inc, number 31837, Nov.
- Thomas R. Cook & Sophia Kazinnik & Anne Lundgaard Hansen & Peter McAdam, 2023, "Evaluating Local Language Models: An Application to Bank Earnings Calls," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 23-12, Nov.
- Bruns-Smith, David & Feller, Avi & Nakamura, Emi, 2023, "Using Supervised Learning to Estimate Inequality in the Size and Persistence of Income Shocks," Department of Economics, Working Paper Series, Department of Economics, Institute for Business and Economic Research, UC Berkeley, number qt1zg3z4mb, Jun.