Report NEP-BIG-2023-12-04

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:

• Xiong Xiong & Fan Yang & Li Su, 2023, "Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques," Papers, arXiv.org, number 2310.19200, Oct.
• Ryan Chipwanya, 2023, "Stock Market Directional Bias Prediction Using ML Algorithms," Papers, arXiv.org, number 2310.16855, Oct.
• Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023, "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, number 23-04, Nov, revised Nov 2023.
• Sangkyu Lee, 2023, "Strategies for Optimizing Policy Outcomes through Machine Learning: A Case Study on Korean R&D Project Assessment," Industrial Economic Review, Korea Institute for Industrial Economics and Trade, number 23-22, Oct.
• Marcelo DEL Cajias & Anna Freudenreich, 2023, "What are tenants demanding the most? A machine learning approach for the prediction of time on market," ERES, European Real Estate Society (ERES), number eres2023_35, Jan.
• Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023, "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics, University of São Paulo (FEA-USP), number 2023_13, Nov.
• Marco Delogu & Raffaelle Lagravinese & Dimitri Paolini & Giuliano Resce, 2020, "Predicting dropout from higher education: Evidence from Italy," DEM Discussion Paper Series, Department of Economics at the University of Luxembourg, number 22-06.
• Nian Si, 2023, "Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach," Papers, arXiv.org, number 2310.17496, Oct, revised Apr 2024.
• Abi Adams-Prassl & Maria Balgova & Matthias Qian & Tom Waters, 2023, "Firm Concentration & Job Design: The Case of Schedule Flexible Work Arrangements," Economics Series Working Papers, University of Oxford, Department of Economics, number 1002, Feb.
• Jonathan Alexander Muñoz-Martínez & David Orozco & Mario A. Ramos-Veloza, 2023, "Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia," Borradores de Economia, Banco de la Republica de Colombia, number 1256, Nov, DOI: 10.32468/be.1256.
• Mariam Dundua & Otar Gorgodze, 2022, "Application of Artificial Intelligence for Monetary Policy-Making," NBG Working Papers, National Bank of Georgia, number 02/2022, Nov.
• Thomas R. Cook & Sophia Kazinnik & Anne Lundgaard Hansen & Peter McAdam, 2023, "Evaluating Local Language Models: An Application to Bank Earnings Calls," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 23-12, Nov.
• Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2023, "Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/23/01.
• Seulki Chung, 2023, "Inside the black box: Neural network-based real-time prediction of US recessions," Papers, arXiv.org, number 2310.17571, Oct, revised May 2024.
• Hendrik Jenett, 2023, "Composition of Real Estate Values: Analyzing Time-Varying Credit and Market Data Using Neural Networks," ERES, European Real Estate Society (ERES), number eres2023_183, Jan.
• Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023, "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers, arXiv.org, number 2311.02789, Nov, revised Apr 2024.
• Antonin Bergeaud & Cyril Verluise, 2022, "The rise of China's technological power: the perspective from frontier technologies," POID Working Papers, Centre for Economic Performance, LSE, number 039, Oct.
• Bastian Krämer & Moritz Stang & Vanja Doskoc & Wolfgang Schäfers & Friedrich Tobias, 2023, "Automated Valuation Models: Improving Model Performance by Choosing the Optimal Spatial Training Level," ERES, European Real Estate Society (ERES), number eres2023_120, Jan.
• Kang Gao & Stephen Weston & Perukrishnen Vytelingum & Namid R. Stillman & Wayne Luk & Ce Guo, 2023, "Deeper Hedging: A New Agent-based Model for Effective Deep Hedging," Papers, arXiv.org, number 2310.18755, Oct.
• Tomas Adam & Jan Belka & Martin Hluze & Jakub Mateju & Hana Prause & Jiri Schwarz, 2023, "Ace in Hand: The Value of Card Data in the Game of Nowcasting," Working Papers, Czech National Bank, Research and Statistics Department, number 2023/14, Oct.
• Tatiana Evdokimova & Piroska Nagy Mohacsi & Olga Ponomarenko & Elina Ribakova, 2023, "Central banks and policy communication: How emerging markets have outperformed the Fed and ECB," Working Paper Series, Peterson Institute for International Economics, number WP23-10, Oct.
• Mamalis, Marios & Kalampokis, Evangelos & Karamanou, Areti & Brimos, Petros & Tarabanis, Konstantinos, 2023, "Can Large Language Models Revolutionalize Open Government Data Portals? A Case of Using ChatGPT in statistics.gov.scot," OSF Preprints, Center for Open Science, number 9b35z, Oct, DOI: 10.31219/osf.io/9b35z.

Report NEP-BIG-2023-12-04

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: