Report NEP-BIG-2021-12-06

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Emanuel Kohlscheen, 2021, "What does machine learning say about the drivers of inflation?," BIS Working Papers, Bank for International Settlements, number 980, Nov.
• Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021, "Predicting Mortality from Credit Reports," Papers, arXiv.org, number 2111.03662, Nov.
• Chaeshick Chung & Sukjin Park, 2021, "Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks," Working Papers, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), number 2108.
• Kaur, Karman & Prasad, Narayan & Prasad, Narayan, 2021, "Modelling Input Energy Used in Wheat Production in India Using Artificial Neural Network," 2021 Conference, August 17-31, 2021, Virtual, International Association of Agricultural Economists, number 315051, Aug, DOI: 10.22004/ag.econ.315051.
• Jelle Barkema & Borislava Mircheva & Mr. Mico Mrkaic & Yuanchen Yang, 2021, "License to Spill: How Do We Discuss Spillovers in Article IV Staff Reports," IMF Working Papers, International Monetary Fund, number 2021/134, May.
• Bhumjai Tangsawasdirat & Suranan Tanpoonkiat & Burasakorn Tangsatchanan, 2021, "Credit Risk Database: Credit Scoring Models for Thai SMEs," PIER Discussion Papers, Puey Ungphakorn Institute for Economic Research, number 168, Nov.
• Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021, "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series, European Central Bank, number 2616, Nov.
• Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021, "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers, arXiv.org, number 2111.04709, Nov.
• Klaus-Peter Hellwig, 2021, "Predicting Fiscal Crises: A Machine Learning Approach," IMF Working Papers, International Monetary Fund, number 2021/150, May.
• Mao Guan & Xiao-Yang Liu, 2021, "Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach," Papers, arXiv.org, number 2111.03995, Nov, revised Dec 2021.
• Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021, "Do search engines increase concentration in media markets?," UB School of Economics Working Papers, University of Barcelona School of Economics, number 2021/415.
• Mizuho Kida & Simon Paetzold, 2021, "The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning," IMF Working Papers, International Monetary Fund, number 2021/153, May.
• Mr. Anton Korinek & Mr. Martin Schindler & Joseph Stiglitz, 2021, "Technological Progress, Artificial Intelligence, and Inclusive Growth," IMF Working Papers, International Monetary Fund, number 2021/166, Jun.
• Gareth Anderson & Paolo Galang & Mr. Andrea Gamba & Leandro Medina & Tianxiao Zheng, 2021, "How Have IMF Priorities Evolved? A Text Mining Approach," IMF Working Papers, International Monetary Fund, number 2021/159, Jun.
• Songqiao Han & Hailiang Huang & Jiangwei Liu & Shengsheng Xiao, 2021, "American Hate Crime Trends Prediction with Event Extraction," Papers, arXiv.org, number 2111.04951, Nov.
• Jakob Mokander, 2021, "On the Limits of Design: What Are the Conceptual Constraints on Designing Artificial Intelligence for Social Good?," Papers, arXiv.org, number 2111.04165, Nov.
• Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2021, "Labour-saving automation and occupational exposure: a text-similarity measure," GLO Discussion Paper Series, Global Labor Organization (GLO), number 987.
• Benjamin Meindl & Morgan R. Frank & Joana Mendonc{c}a, 2021, "Exposure of occupations to technologies of the fourth industrial revolution," Papers, arXiv.org, number 2110.13317, Oct.
• Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2021, "Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach," Working Papers, University of Pretoria, Department of Economics, number 202182, Nov.
• Anton Angelgardt & Elena S. Gorbunova & Maria Chumakova, 2021, "An Assessment Of Trust In Artificial Intelligent Agents: Tool Development," HSE Working papers, National Research University Higher School of Economics, number WP BRP 128/PSY/2021.
• Corredera Barbado, Alberto & Ruiz Mora, Carlos, 2022, "Prescriptive selection of machine learning hyperparameters with applications in power markets: retailer's optimal trading," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 33693, Oct.
• Alexander Kell, 2021, "Modelling the transition to a low-carbon energy supply," Papers, arXiv.org, number 2111.00987, Sep.
• Jeronymo Marcondes Pinto & Jennifer L. Castle, 2021, "A machine learning dynamic switching approach to forecasting when there are structural breaks," Economics Series Working Papers, University of Oxford, Department of Economics, number 950 JEL classification: C, Oct.
• Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021, "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers, arXiv.org, number 2111.05188, Nov.
• Anders Nõu & Darya Lapitskaya & Mustafa Hakan Eratalay & Rajesh Sharma, 2021, "Predicting Stock Return And Volatility With Machine Learning And Econometric Models: A Comparative Case Study Of The Baltic Stock Market," University of Tartu - Faculty of Economics and Business Administration Working Paper Series, Faculty of Economics and Business Administration, University of Tartu (Estonia), number 135.
• Aleksandra Parteka & Joanna Wolszczak-Derlacz & Dagmara Nikulin, 2021, "How digital technology affects working conditions in globally fragmented production chains: evidence from Europe," GUT FME Working Paper Series A, Faculty of Management and Economics, Gdansk University of Technology, number 66, Nov.
• Simon B chler, Maximilian v. Ehrlich, 2021, "Quantifying Land Use Regulation and its Determinants - Ease of Residential Development across Swiss Municipalities," Diskussionsschriften, Universitaet Bern, Departement Volkswirtschaft - CRED, number credresearchpaper32, Aug.
• Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021, "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series, European Central Bank, number 2614, Nov.
• Brandon Buell & Reda Cherif & Carissa Chen & Karl Walentin & Jiawen Tang & Nils Wendt, 2021, "Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa," IMF Working Papers, International Monetary Fund, number 2021/124, May.

Report NEP-BIG-2021-12-06

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: