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 (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report:

• Giovanni Cerulli, 2021, "Machine learning using Stata/Python," 2021 Stata Conference, Stata Users Group, number 25, Aug.
• Simon Blöthner & Mario Larch, 2021, "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series, CESifo, number 9233.
• 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.
• 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.
• 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.
• 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.
• 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.
• Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021, "Comparing minds and machines: implications for financial stability," Bank of England working papers, Bank of England, number 937, Aug.
• 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.
• Qi Feng & Man Luo & Zhaoyu Zhang, 2021, "Deep Signature FBSDE Algorithm," Papers, arXiv.org, number 2108.10504, Aug, 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, Agricultural and Applied Economics Association, number 312764, Aug, DOI: 10.22004/ag.econ.312764.
• 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.
• 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.
• Lin William Cong & Ke Tang & Jingyuan Wang & Yang Zhang, 2021, "Deep Sequence Modeling: Development and Applications in Asset Pricing," Papers, arXiv.org, number 2108.08999, 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.
• 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.
• 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, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2114, May.
• 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, Departamento de Economía - Pontificia Universidad Católica del Perú, number 2021-497, DOI: 10.18800/2079-8474.0497.
• 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, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge, number EPRG2030, Nov.
• Sung Hoon Choi, 2021, "Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia," Papers, arXiv.org, number 2108.10250, Aug, 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, arXiv.org, number 2108.09520, Aug, 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, University Library of Munich, Germany, number 109117, Sep.
• 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.
• 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, ZEW - Leibniz Centre for European Economic Research, number 21-062.
• 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, Agricultural and Applied Economics Association, number 312878, Aug, DOI: 10.22004/ag.econ.312878.

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 (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: