Report NEP-CMP-2021-02-01

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

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

Other reports in NEP-CMP

The following items were announced in this report:

• Jillian M. Clements & Di Xu & Nooshin Yousefi & Dmitry Efimov, 2020, "Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data," Papers, arXiv.org, number 2012.15330, Dec.
• Gang Huang & Xiaohua Zhou & Qingyang Song, 2020, "Deep Reinforcement Learning for Long-Short Portfolio Optimization," Papers, arXiv.org, number 2012.13773, Dec, revised Mar 2025.
• Ho, Mun & Britz, Wolfgang & Delzeit, Ruth & Leblanc, Florian & Roson, Roberto & Schuenemann, Franziska & Weitzel, Matthias, 2020, "Modelling Consumption and Constructing Long-Term Baselines in Final Demand," Open Access Publications from Kiel Institute for the World Economy, Kiel Institute for the World Economy, number 228656, DOI: 10.21642/JGEA.050103AF.
• Ali R. Baghirzade, 2020, "Development of cloud, digital technologies and the introduction of chip technologies," Papers, arXiv.org, number 2012.08864, Dec.
• Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021, "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali, number 450, Jan.
• Delzeit, Ruth & Beach, Robert & Bibas, Ruben & Britz, Wolfgang & Chateau, Jean & Freund, Florian & Lefevre, Julien & Schuenemann, Franziska & Sulser, Timothy & Valin, Hugo & van Ruijven, Bas & Weitzel, 2020, "Linking Global CGE models with Sectoral Models to Generate Baseline Scenarios: Approaches, Challenges, and Opportunities," Open Access Publications from Kiel Institute for the World Economy, Kiel Institute for the World Economy, number 228648, DOI: 10.21642/JGEA.050105AF.
• Leroy de Morel Laëtitia & Wittwer Glen & Leung Christina & Gämperle Dion, 2020, "The potential local and regional impacts of COVID-19 in New Zealand: with a focus on tourism," NZIER Working Paper, New Zealand Institute of Economic Research, number 2020/3, Aug.
• Kenneth Lomas & Dave Cliff, 2020, "Exploring Narrative Economics: An Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics," Papers, arXiv.org, number 2012.08840, Dec.
• Syed Badruddoza & Modhurima Amin & Jill McCluskey, 2019, "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers, School of Economic Sciences, Washington State University, number 2019-5, Dec.
• Sondes Kahouli & Xavier Pautrel, 2020, "Residential and Industrial Energy Efficiency Improvement: A Dynamic General Equilibrium Analysis of the Rebound Effect," Working Papers, Fondazione Eni Enrico Mattei, number 2020.28, Dec.
• Zhao, Bingyu & Wong, Stephen D, 2021, "Developing Transportation Response Strategies for Wildfire Evacuations via an Empirically Supported Traffic Simulation of Berkeley, California," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings, Institute of Transportation Studies, UC Berkeley, number qt70p6k4rf, Aug.
• Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020, "Adversarial Estimation of Riesz Representers," Papers, arXiv.org, number 2101.00009, Dec, revised Apr 2024.
• Mochen Yang & Edward McFowland III & Gordon Burtch & Gediminas Adomavicius, 2020, "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," Papers, arXiv.org, number 2012.10790, Dec.
• Mehmet Caner & Kfir Eliaz, 2021, "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers, arXiv.org, number 2101.01144, Jan, revised Sep 2021.
• Tzougas, George & Jeong, Himchan, 2021, "An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 108210, Jan.
• Mateusz Denys, 2020, "Model of cunning agents," Papers, arXiv.org, number 2012.08517, Dec.
• Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021, "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers, Bank for International Settlements, number 917, Jan.
• Jeffrey Grogger & Ria Ivandic & Tom Kirchmaier, 2020, "In brief...Tackling domestic violence using machine learning," CentrePiece - The magazine for economic performance, Centre for Economic Performance, LSE, number 579, Jul.
• Mesbah, Neda & Tauchert, Christoph & Buxmann, Peter, 2021, "Whose Advice Counts More – Man or Machine? An Experimental Investigation of AI-based Advice Utilization," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 124796, Jan.
• Nan Hu & Jian Li & Alexis Meyer-Cirkel, 2019, "Completing the Market: Generating Shadow CDS Spreads by Machine Learning," IMF Working Papers, International Monetary Fund, number 2019/292, Dec.
• Anna Baiardi & Andrea A. Naghi, 2021, "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers, arXiv.org, number 2101.00878, Jan.
• Zhichao Wang & Valentin Zelenyuk, 2021, "Performance Analysis of Hospitals in Australia and its Peers: A Systematic Review," CEPA Working Papers Series, School of Economics, University of Queensland, Australia, number WP012021, Jan.
• Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021, "Measuring national happiness with music," The Warwick Economics Research Paper Series (TWERPS), University of Warwick, Department of Economics, number 1326.
• Vladimir Vargas-Calder'on & Jorge E. Camargo, 2020, "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy," Papers, arXiv.org, number 2012.09115, Nov.
• Rauh, C. & Renée, L., 2021, "Parenting Types," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2110, Jan.
• James Chapman & Ajit Desai, 2021, "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers, Bank of Canada, number 21-2, Jan, DOI: 10.34989/swp-2021-2.
• Ian Burn & Daniel Firoozi & Daniel Ladd & David Neumark, 2021, "Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment," NBER Working Papers, National Bureau of Economic Research, Inc, number 28328, Jan.
• Pierre Durand & Gaëtan Le Quang, 2021, "What do bankrupcty prediction models tell us about banking regulation? Evidence from statistical and learning approaches," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2021-2.
• Oz Shy, 2020, "Alternative Methods for Studying Consumer Payment Choice," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2020-8, Jun, DOI: 10.29338/wp2020-08.
• Le Mezo, Helena & Ferrari Minesso, Massimo, 2021, "Text-based recession probabilities," Working Paper Series, European Central Bank, number 2516, Jan.
• Merino Troncoso, Carlos, 2021, "Consumer Demand Estimation," MPRA Paper, University Library of Munich, Germany, number 105169, Jan.

Report NEP-CMP-2021-02-01

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.

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

Other reports in NEP-CMP

The following items were announced in this report: