Report NEP-CMP-2022-09-05

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stan Miles issued this report. It is usually issued weekly.

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Other reports in NEP-CMP

The following items were announced in this report:

• Giovanni Cerulli, 2022. "Machine learning using Stata/Python," Italian Stata Users' Group Meetings 2022 02, Stata Users Group.
• Leonardo Kanashiro Felizardo & Elia Matsumoto & Emilio Del-Moral-Hernandez, 2022. "Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise," Papers 2208.00765, arXiv.org.
• Baier, Scott & Regmi, Narendra, 2021. "Using Machine Learning to Capture Heterogeneity in Trade Agreements," Working Papers 11027, George Mason University, Mercatus Center.
• Qin, Fei & Wu, Steven Y., 2022. "Estimating Consumer Segments and Choices from Limited Information: The Application of Machine Learning Methods," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322473, Agricultural and Applied Economics Association.
• Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022. "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series 561, Central Bank of Brazil, Research Department.
• Askery Canabarro & Taysa M. Mendonc{c}a & Ranieri Nery & George Moreno & Anton S. Albino & Gleydson F. de Jesus & Rafael Chaves, 2022. "Quantum Finance: a tutorial on quantum computing applied to the financial market," Papers 2208.04382, arXiv.org, revised Aug 2022.
• Mostafa Shabani & Dat Thanh Tran & Juho Kanniainen & Alexandros Iosifidis, 2022. "Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification," Papers 2207.11577, arXiv.org.
• José Manuel Carbó & Sergio Gorjón, 2022. "Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin," Working Papers 2215, Banco de España.
• Tianchen Zhao & Chuhao Sun & Asaf Cohen & James Stokes & Shravan Veerapaneni, 2022. "Quantum-inspired variational algorithms for partial differential equations: Application to financial derivative pricing," Papers 2207.10838, arXiv.org.
• Daniel Goller & Andrea Diem & Stefan C. Wolter, 2022. "Sitting Next to a Dropout - Academic Success of Students with More Educated Peers," CESifo Working Paper Series 9812, CESifo.
• Buhlmann, Florian & Hebsaker, Michael & Kreuz, Tobias & Schmidhäuser, Jakob & Siegloch, Sebastian & Stichnoth, Holger, 2022. "ZEW-EviSTA: A microsimulation model of the German tax and transfer system," ZEW Discussion Papers 22-026, ZEW - Leibniz Centre for European Economic Research.
• Jherek Healy, 2022. "Accurate and consistent calculation of the mean and variance in Monte-Carlo simulations," Papers 2206.10662, arXiv.org, revised Sep 2022.
• Xi-Ning Zhuang & Zhao-Yun Chen & Yu-Chun Wu & Guo-Ping Guo, 2021. "Quantum Quantitative Trading: High-Frequency Statistical Arbitrage Algorithm," Papers 2104.14214, arXiv.org.
• Bora, Siddhartha S. & Katchova, Ani, 2022. "Multi-horizon Forecasts of Agricultural Commodity Prices using Deep Learning," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322557, Agricultural and Applied Economics Association.
• Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2022. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," NBER Working Papers 30302, National Bureau of Economic Research, Inc.
• Aarts, Emile, 2022. "Artificial Intelligence : up to here ... and on again," Other publications TiSEM d656c4b4-c3ea-4700-8c93-0, Tilburg University, School of Economics and Management.
• Shaswat Mohanty & Anirudh Vijay & Nandagopan Gopakumar, 2022. "StockBot: Using LSTMs to Predict Stock Prices," Papers 2207.06605, arXiv.org, revised Jul 2022.