Report NEP-CMP-2020-08-24

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:

• Yang Li & Yi Pan, 2020, "A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News," Papers, arXiv.org, number 2007.12620, Jul.
• Bauermann, Tom & Roos, Michael W. M. & Schaff, Frederik, 2020, "POSA: Policy implementation sensitivity analysis," Ruhr Economic Papers, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 854, DOI: 10.4419/86788990.
• Chao Deng & Xizhi Su & Chao Zhou, 2020, "Relative wealth concerns with partial information and heterogeneous priors," Papers, arXiv.org, number 2007.11781, Jul.
• Taran Fæhn & Gabriel Bachner & Robert Beach & Jean Chateau & Shinichiro Fujimori & Madanmohan Ghosh & Meriem Hamdi-Cherif & Elisa Lanzi & Sergey Paltsev & Toon Vandyck & Bruno Cunha & Rafael Garaffa &, 2020, "Capturing Key Energy and Emission Trends in CGE models. Assessment of Status and Remaining Challenges," Discussion Papers, Statistics Norway, Research Department, number 936, Jul.
• Alessandro Gnoatto & Athena Picarelli & Christoph Reisinger, 2020, "Deep xVA solver - A neural network based counterparty credit risk management framework," Working Papers, University of Verona, Department of Economics, number 07/2020, May.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working papers, University of Connecticut, Department of Economics, number 2020-10, Aug.
• A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020, "Machine Learning approach for Credit Scoring," Papers, arXiv.org, number 2008.01687, Jul.
• Wen Chen & Nicolas Langren'e, 2020, "Deep neural network for optimal retirement consumption in defined contribution pension system," Papers, arXiv.org, number 2007.09911, Jul, revised Jul 2020.
• Dominique Guegan, 2020, "A Note on the Interpretability of Machine Learning Algorithms," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL, number halshs-02900929, Jul.
• Christoph Böhringer & Knut Einar Rosendahl, 2020, "Europe beyond Coal - An Economic and Climate Impact Assessment," CESifo Working Paper Series, CESifo, number 8412.
• Carlo Baldassi & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020, "Ergodic Annealing," Papers, arXiv.org, number 2008.00234, Aug.
• Yongtong Shao & Minghao Li & Dermot J. Hayes & Wendong Zhang & Tao Xiong & Wei Xie, 2020, "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," Center for Agricultural and Rural Development (CARD) Publications, Center for Agricultural and Rural Development (CARD) at Iowa State University, number 20-wp607, Aug.
• Tine Hufkens & Tim Goedemé & Katrin Gasior & Chrysa Leventi & Kostas Manios & Olga Rastrigina & Pasquale Recchia & Holly Sutherland & Natascha Van Mechelen & Gerlinde Verbist, 2018, "The Hypothetical Household Tool (HHoT) in EUROMOD: a new instrument for comparative research on tax-benefit policies in Europe," Working Papers, Herman Deleeck Centre for Social Policy, University of Antwerp, number 1819, Dec.
• Zhenzhong Wang & Zhengyuan Zhu & Cindy Yu, 2020, "Variable Selection in Macroeconomic Forecasting with Many Predictors," Papers, arXiv.org, number 2007.10160, Jul.
• Jamaledini, Ashkan & Soltani, Ali & Khazaei, Ehsan, 2020, "Region Search Optimization Algorithm for Economic Energy Management of Grid-Connected Mode Microgrid," MPRA Paper, University Library of Munich, Germany, number 102094, Mar.
• Andrew J. Collins & Sheida Etemadidavan & Wael Khallouli, 2020, "Generating Empirical Core Size Distributions of Hedonic Games using a Monte Carlo Method," Papers, arXiv.org, number 2007.12127, Jul.
• Marcin Chlebus & Michał Dyczko & Michał Woźniak, 2020, "Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-22.
• Ivan Slobozhan & Peter Ormosi & Rajesh Sharma, 2020, "Which bills are lobbied? Predicting and interpreting lobbying activity in the US," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP), Centre for Competition Policy, University of East Anglia, Norwich, UK., number 2020-03, Jan.
• Ivan Guo & Nicolas Langrené & Gregoire Loeper & Wei Ning, 2020, "Robust utility maximization under model uncertainty via a penalization approach," Working Papers, HAL, number hal-02910261, Aug.
• Illya Barziy & Marcin Chlebus, 2020, "HRP performance comparison in portfolio optimization under various codependence and distance metrics," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-21.
• Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020, "Deep Dynamic Factor Models," Papers, arXiv.org, number 2007.11887, Jul, revised May 2023.

Report NEP-CMP-2020-08-24

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: