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Report NEP-BIG-2021-02-15
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:
- Majid Bazarbash, 2019, "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers, International Monetary Fund, number 2019/109, May.
- Christian M. Dahl & Torben Johansen & Emil N. S{o}rensen & Simon Wittrock, 2021, "HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition," Papers, arXiv.org, number 2101.10862, Jan, revised Mar 2022.
- Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021, "Discrete Choice Analysis with Machine Learning Capabilities," Papers, arXiv.org, number 2101.10261, Jan.
- Yi Wei, 2021, "Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction," Papers, arXiv.org, number 2101.10942, Jan, revised Mar 2021.
- Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2021, "Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach," Papers, arXiv.org, number 2101.09394, Jan, revised Jan 2022.
- Pierre-Jean Benghozi & Hugues Chevalier, 2019, "The present vision of AI… or the HAL syndrome," Post-Print, HAL, number hal-03101149, May, DOI: 10.1108/dprg-12-2018-0079.
- John Ery & Loris Michel, 2021, "Solving optimal stopping problems with Deep Q-Learning," Papers, arXiv.org, number 2101.09682, Jan, revised Jun 2024.
- Mark Kiermayer, 2021, "Modeling surrender risk in life insurance: theoretical and experimental insight," Papers, arXiv.org, number 2101.11590, Jan, revised Aug 2021.
- Jeffrey Grogger & Ria Ivandic & Tom Kirchmaier, 2020, "Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases," CEP Discussion Papers, Centre for Economic Performance, LSE, number dp1676, Feb.
- Vera Eichenauer & Ronald Indergand & Isabel Z. MartÃnez & Christoph Sax, 2020, "Constructing Daily Economic Sentiment Indices Based on Google Trends," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 20-484, Jun, DOI: 10.3929/ethz-b-000423548.
- Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021, "Measuring National Happiness with Music," CAGE Online Working Paper Series, Competitive Advantage in the Global Economy (CAGE), number 537.
- In-Koo Cho & Jonathan Libgober, 2021, "Machine Learning for Strategic Inference," Papers, arXiv.org, number 2101.09613, Jan.
- Bart H. L. Overes & Michel van der Wel, 2021, "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers, arXiv.org, number 2101.12684, Jan, revised Jul 2021.
- Ines Wilms & Jacob Bien, 2021, "Tree-based Node Aggregation in Sparse Graphical Models," Papers, arXiv.org, number 2101.12503, Jan.
- Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021, "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series, CESifo, number 8835.
- Tommaso Proietti & Alessandro Giovannelli, 2020, "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper, Tor Vergata University, CEIS, number 482, May, revised 12 May 2020.
- Michele Cantarella & Nicolò Fraccaroli & Roberto Volpe, 2020, "Does Fake News Affect Voting Behaviour?," CEIS Research Paper, Tor Vergata University, CEIS, number 493, Jun, revised 17 Jun 2020.
- Jansen, Mark & Nguyen, Hieu & Shams, Amin, 2020, "Rise of the Machines: The Impact of Automated Underwriting," Working Paper Series, Ohio State University, Charles A. Dice Center for Research in Financial Economics, number 2020-19, Jul.
- Chengyu Huang & Sean Simpson & Daria Ulybina & Agustin Roitman, 2019, "News-based Sentiment Indicators," IMF Working Papers, International Monetary Fund, number 2019/273, Dec.
- Shree Saha & Sudha Narayanan, 2020, "A Simplified measure of nutritional empowerment using machine learning to abbreviate the Women's Empowerment in Nutrition Index (WENI)," Indira Gandhi Institute of Development Research, Mumbai Working Papers, Indira Gandhi Institute of Development Research, Mumbai, India, number 2020-031, Oct.