Report NEP-CMP-2021-08-30

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:

• Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
• Байкулаков Шалкар // Baikulakov Shalkar & Белгибаев Зангар // Belgibayev Zanggar, 2021. "Анализ рисков потребительских кредитов с помощью алгоритмов машинного обучения // Consumer credit risk analysis via machine learning algorithms," Working Papers #2021-4, National Bank of Kazakhstan.
• Simon Blöthner & Mario Larch, 2021. "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series 9233, CESifo.
• Giovanni Cerulli, 2021. "Machine learning using Stata/Python," 2021 Stata Conference 25, Stata Users Group.
• Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
• Meerza, Syed Imran Ali & Brooks, Kathleen R. & Gustafson, Christopher R. & Yiannaka, Amalia, 2021. "Predicting Information Avoidance Behavior using Machine Learning," 2021 Annual Meeting, August 1-3, Austin, Texas 312876, Agricultural and Applied Economics Association.
• Gabriel de Oliveira Guedes Nogueira & Marcel Otoboni de Lima, 2021. "Previs\~ao dos pre\c{c}os de abertura, m\'inima e m\'axima de \'indices de mercados financeiros usando a associa\c{c}\~ao de redes neurais LSTM," Papers 2108.10065, arXiv.org.
• International Monetary Fund, 2021. "How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning," IMF Technical Notes and Manuals 2021/003, International Monetary Fund.
• Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
• Paul Hunermund & Beyers Louw & Itamar Caspi, 2021. "Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale," Papers 2108.11294, arXiv.org, revised May 2023.
• Ramit Debnath & Sarah Darby & Ronita Bardhan & Kamiar Mohaddes & Minna Sunikka-Blank, 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Working Papers EPRG2019, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
• Qi Feng & Man Luo & Zhaoyu Zhang, 2021. "Deep Signature FBSDE Algorithm," Papers 2108.10504, arXiv.org, revised Aug 2022.
• Ludovic Gouden`ege & Andrea Molent & Antonino Zanette, 2021. "Moving average options: Machine Learning and Gauss-Hermite quadrature for a double non-Markovian problem," Papers 2108.11141, arXiv.org.
• Parvez, Rezwanul & Ali Meerza, Syed Imran & Hasan Khan Chowdhury, Nazea, 2021. "Forecasting student enrollment using time series models and recurrent neural networks," 2021 Annual Meeting, August 1-3, Austin, Texas 312912, Agricultural and Applied Economics Association.
• David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021. "Loss-Based Variational Bayes Prediction," Monash Econometrics and Business Statistics Working Papers 8/21, Monash University, Department of Econometrics and Business Statistics.
• Sebastian Jaimungal & Silvana Pesenti & Ye Sheng Wang & Hariom Tatsat, 2021. "Robust Risk-Aware Reinforcement Learning," Papers 2108.10403, arXiv.org, revised Dec 2021.
• Mr. Zamid Aligishiev & Mr. Giovanni Melina & Luis-Felipe Zanna, 2021. "DIGNAR-19 Toolkit Manual," IMF Technical Notes and Manuals 2021/007, International Monetary Fund.
• Amin, Modhurima D. & Badruddoza, Syed & Mantle, Steve, 2021. "Applying Artificial Intelligence in Agriculture: Evidence from Washington State Apple Orchards," 2021 Annual Meeting, August 1-3, Austin, Texas 312764, Agricultural and Applied Economics Association.
• Yixiao Lu & Yihong Wang & Tinggan Yang, 2021. "Adaptive Gradient Descent Methods for Computing Implied Volatility," Papers 2108.07035, arXiv.org, revised Mar 2023.