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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. 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:
- Martin Baumgaertner & Johannes Zahner, 2021, "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202130.
- 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, National Bank of Kazakhstan, number #2021-4.
- Simon Blöthner & Mario Larch, 2021, "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series, CESifo, number 9233.
- Giovanni Cerulli, 2021, "Machine learning using Stata/Python," 2021 Stata Conference, Stata Users Group, number 25, Aug.
- 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, University Library of Munich, Germany, number 109137, Apr.
- 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, Agricultural and Applied Economics Association, number 312876, Aug, DOI: 10.22004/ag.econ.312876.
- 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, arXiv.org, number 2108.10065, Jul.
- International Monetary Fund, 2021, "How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning," IMF Technical Notes and Manuals, International Monetary Fund, number 2021/003, May.
- Jieyi Kang & David Reiner, 2021, "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2113, May.
- Paul Hunermund & Beyers Louw & Itamar Caspi, 2021, "Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale," Papers, arXiv.org, number 2108.11294, Aug, 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, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2019, Jul.
- Qi Feng & Man Luo & Zhaoyu Zhang, 2021, "Deep Signature FBSDE Algorithm," Papers, arXiv.org, number 2108.10504, Aug, 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, arXiv.org, number 2108.11141, Aug.
- 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, Agricultural and Applied Economics Association, number 312912, Aug, DOI: 10.22004/ag.econ.312912.
- David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin & Bonsoo Koo, 2021, "Loss-Based Variational Bayes Prediction," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 8/21.
- Sebastian Jaimungal & Silvana Pesenti & Ye Sheng Wang & Hariom Tatsat, 2021, "Robust Risk-Aware Reinforcement Learning," Papers, arXiv.org, number 2108.10403, Aug, revised Dec 2021.
- Mr. Zamid Aligishiev & Mr. Giovanni Melina & Luis-Felipe Zanna, 2021, "DIGNAR-19 Toolkit Manual," IMF Technical Notes and Manuals, International Monetary Fund, number 2021/007, Jun.
- 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, Agricultural and Applied Economics Association, number 312764, Aug, DOI: 10.22004/ag.econ.312764.
- Yixiao Lu & Yihong Wang & Tinggan Yang, 2021, "Adaptive Gradient Descent Methods for Computing Implied Volatility," Papers, arXiv.org, number 2108.07035, Aug, revised Mar 2023.