Report NEP-BIG-2021-08-30

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Giovanni Cerulli, 2021. "Machine learning using Stata/Python," 2021 Stata Conference 25, Stata Users Group.
• Simon Blöthner & Mario Larch, 2021. "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series 9233, CESifo.
• 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.
• 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.
• 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.
• 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.
• 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.
• Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.
• 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.
• Qi Feng & Man Luo & Zhaoyu Zhang, 2021. "Deep Signature FBSDE Algorithm," Papers 2108.10504, arXiv.org, revised Aug 2022.
• 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.
• 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.
• 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.
• Lin William Cong & Ke Tang & Jingyuan Wang & Yang Zhang, 2021. "Deep Sequence Modeling: Development and Applications in Asset Pricing," Papers 2108.08999, 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.
• 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.
• Jieyi Kang & David Reiner, 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers EPRG2114, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
• Marco Vega & Erick Lahura & Hilary Coronado, 2021. "¿Qué tan rígidos son los precios en línea? Evidencia para Perú usando Big Data," Documentos de Trabajo / Working Papers 2021-497, Departamento de Economía - Pontificia Universidad Católica del Perú.
• Ramit Debnath & Ronita Bardhan & Sarah Darby & Kamiar Mohaddes & Minna Sunikka-Blank, 2020. "A deep-narrative analysis of energy cultures in slum rehabilitation housing of Abuja, Mumbai and Rio de Janeiro for just policy design," Working Papers EPRG2030, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
• Sung Hoon Choi, 2021. "Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia," Papers 2108.10250, arXiv.org, revised May 2022.
• Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
• Rughinis, Razvan & Rughinis, Cosima & Vulpe, Simona Nicoleta & Rosner, Daniel, 2021. "From social netizens to data citizens: variations of GDPR awareness in 28 European countries," MPRA Paper 109117, University Library of Munich, Germany.
• 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.
• Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021. "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers 21-062, ZEW - Leibniz Centre for European Economic Research.
• Li, Ran & Xu, Yuetong & Chen, Jian & Qi, Danyi, 2021. "Meal Kit Preferences during COVID-19 Pandemic: Exploring User-Generated Content with Natural Language Processing Techniques," 2021 Annual Meeting, August 1-3, Austin, Texas 312878, Agricultural and Applied Economics Association.