Report NEP-BIG-2022-05-02

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:

• John Gibson & Bonggeun Kim & Geua Boe-Gibson, 2022, "How effective are sanctions on North Korea? Popular DMSP night-lights data may bias evaluations due to blurring and poor low-light detection," Working Papers in Economics, University of Waikato, number 22/06, Mar, revised 14 Nov 2022.
• Elodie Blanc & Ilan Noy, 2022, "Impacts of Droughts and Floods on Agricultural Productivity in New Zealand as Measured from Space," CESifo Working Paper Series, CESifo, number 9634.
• Melissa Newham & Marica Valente, 2022, "The Cost of Influence: How Gifts to Physicians Shape Prescriptions and Drug Costs," Papers, arXiv.org, number 2203.01778, Mar, revised Apr 2023.
• Laureti, Lucio & Costantiello, Alberto & Leogrande, Angelo, 2022, "Satisfaction with the Environmental Condition in the Italian Regions between 2004 and 2020," MPRA Paper, University Library of Munich, Germany, number 112460, Mar.
• Javad T. Firouzjaee & Pouriya Khaliliyan, 2022, "Machine learning model to project the impact of Ukraine crisis," Papers, arXiv.org, number 2203.01738, Mar.
• Philippe Cotte & Pierre Lagier & Vincent Margot & Christophe Geissler, 2022, "Making use of supercomputers in financial machine learning," Papers, arXiv.org, number 2203.00427, Mar.
• Cameron Fen & Samir Undavia, 2022, "Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model," Papers, arXiv.org, number 2203.06540, Mar.
• Raad Khraishi & Ramin Okhrati, 2022, "Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit," Papers, arXiv.org, number 2203.03003, Mar.
• Yuanrong Wang & Tomaso Aste, 2022, "Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series," Papers, arXiv.org, number 2203.03991, Mar.
• Ishu Gupta & Tarun Kumar Madan & Sukhman Singh & Ashutosh Kumar Singh, 2022, "HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model," Papers, arXiv.org, number 2203.08143, Mar.
• FERNANDEZ LLORCA David & GOMEZ Emilia, 2022, "Artificial Intelligence in Autonomous Vehicles: towards trustworthy systems," JRC Research Reports, Joint Research Centre, number JRC128170, Mar.
• Sarracino, Francesco & Greyling, Talita & O'Connor, Kelsey J. & Peroni, Chiara & Rossouw, Stephanie, 2022, "Trust Predicts Compliance with COVID-19 Containment Policies: Evidence from Ten Countries Using Big Data," IZA Discussion Papers, IZA Network @ LISER, number 15171, Mar.
• Resce, Giuliano & Vaquero-Pineiro, Cristina, 2022, "Predicting Agri-food Quality across Space: A Machine Learning Model for the Acknowledgment of Geographical Indications," Economics & Statistics Discussion Papers, University of Molise, Department of Economics, number esdp22082, Apr.
• Janos Ferencz & Javier López González & Irene Oliván García, 2022, "Artificial Intelligence and international trade: Some preliminary implications," OECD Trade Policy Papers, OECD Publishing, number 260, Apr, DOI: 10.1787/13212d3e-en.
• Keyon Vafa & Emil Palikot & Tianyu Du & Ayush Kanodia & Susan Athey & David M. Blei, 2022, "CAREER: A Foundation Model for Labor Sequence Data," Papers, arXiv.org, number 2202.08370, Feb, revised Feb 2024.
• Ariel Neufeld & Julian Sester & Daiying Yin, 2022, "Detecting data-driven robust statistical arbitrage strategies with deep neural networks," Papers, arXiv.org, number 2203.03179, Mar, revised Feb 2024.
• Timo Dimitriadis & Lutz Duembgen & Alexander Henzi & Marius Puke & Johanna Ziegel, 2022, "Honest calibration assessment for binary outcome predictions," Papers, arXiv.org, number 2203.04065, Mar, revised Nov 2022.
• Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022, "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers, arXiv.org, number 2203.01068, Mar.
• Beraja, Martin & Kao, Andrew & Yang, David Y. & Yuchtman, Noam, 2021, "AI-tocracy," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 113834, Nov.
• Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022, "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers, arXiv.org, number 2203.13887, Mar, revised Jun 2023.
• Aleksandra Parteka & Aleksandra Kordalska, 2022, "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," GUT FME Working Paper Series A, Faculty of Management and Economics, Gdansk University of Technology, number 67, Jan, revised Sep 2022.
• Cameron Fen, 2022, "Fast Simulation-Based Bayesian Estimation of Heterogeneous and Representative Agent Models using Normalizing Flow Neural Networks," Papers, arXiv.org, number 2203.06537, Mar.
• Isaiah Hull & Anna Grodecka-Messi, 2022, "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers, arXiv.org, number 2203.14751, Mar.
• Narayana Darapaneni & Anwesh Reddy Paduri & Himank Sharma & Milind Manjrekar & Nutan Hindlekar & Pranali Bhagat & Usha Aiyer & Yogesh Agarwal, 2022, "Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets," Papers, arXiv.org, number 2204.05783, Apr.
• Mufhumudzi Muthivhi & Terence L. van Zyl, 2022, "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers, arXiv.org, number 2203.05673, Mar.
• Fedor Zagumennov, 2021, "In-Firm Planning and Business Processes Management Using Deep Neural Networks," GATR Journals, Global Academy of Training and Research (GATR) Enterprise, number jber213, Dec, DOI: https://doi.org/10.35609/jber.2021..
• Gian Maria Campedelli, 2022, "Explainable Machine Learning for Predicting Homicide Clearance in the United States," Papers, arXiv.org, number 2203.04768, Mar.
• Jase Clarkson & Mihai Cucuringu & Andrew Elliott & Gesine Reinert, 2022, "DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series," Papers, arXiv.org, number 2203.15009, Mar, revised Oct 2023.
• Md Rashidul Hasan & Muntasir A Kabir & Rezoan A Shuvro & Pankaz Das, 2022, "A Comparative Study on Forecasting of Retail Sales," Papers, arXiv.org, number 2203.06848, Mar.
• Deborah Sulem & Henry Kenlay & Mihai Cucuringu & Xiaowen Dong, 2022, "Graph similarity learning for change-point detection in dynamic networks," Papers, arXiv.org, number 2203.15470, Mar.
• Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022, "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers, arXiv.org, number 2203.03342, Mar, revised Nov 2022.
• Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022, "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers, arXiv.org, number 2203.03613, Mar, revised Jan 2023.
• Pok Wah Chan, 2022, "DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies," Papers, arXiv.org, number 2203.08144, Mar.
• Federico Cornalba & Constantin Disselkamp & Davide Scassola & Christopher Helf, 2022, "Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset trading," Papers, arXiv.org, number 2203.04579, Mar, revised Feb 2023.
• Pedro Cadahia & Antonio A. Golpe & Juan M. Mart'in 'Alvarez & E. Asensio, 2022, "Measuring anomalies in cigarette sales by using official data from Spanish provinces: Are there only the anomalies detected by the Empty Pack Surveys (EPS) used by Transnational Tobacco Companies (TTCs)?," Papers, arXiv.org, number 2203.06640, Mar.
• Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022, "Machine Learning Simulates Agent-Based Model Towards Policy," Papers, arXiv.org, number 2203.02576, Mar, revised Nov 2022.

Report NEP-BIG-2022-05-02

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: