Report NEP-CMP-2021-01-25

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

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Other reports in NEP-CMP

The following items were announced in this report:

• Cogliano, Jonathan F. & Veneziani, Roberto & Yoshihara, Naoki, 2020. "Computational Methods and Classical-Marxian Economics," Discussion Paper Series 716, Institute of Economic Research, Hitotsubashi University.
• Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
• Dong An & Noah Linden & Jin-Peng Liu & Ashley Montanaro & Changpeng Shao & Jiasu Wang, 2020. "Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance," Papers 2012.06283, arXiv.org, revised Jun 2021.
• Jiequn Han & Ruimeng Hu, 2021. "Recurrent Neural Networks for Stochastic Control Problems with Delay," Papers 2101.01385, arXiv.org, revised Jun 2021.
• Elizabeth Jane Casabianca & Alessia Lo Turco & Daniela Maggioni, 2021. "Migration And The Structure Of Manufacturing Production. A View From Italian Provinces," Working Papers 448, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
• Mariano Zeron & Ignacio Ruiz, 2020. "Tensoring volatility calibration," Papers 2012.07440, arXiv.org, revised Dec 2020.
• Kathrin Glau & Linus Wunderlich, 2020. "The Deep Parametric PDE Method: Application to Option Pricing," Papers 2012.06211, arXiv.org.
• Santosh Kumar Radha, 2021. "Quantum option pricing using Wick rotated imaginary time evolution," Papers 2101.04280, arXiv.org.
• Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
• Tamara, Novian & Dwi Muchisha, Nadya & Andriansyah, Andriansyah & Soleh, Agus M, 2020. "Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms," MPRA Paper 105235, University Library of Munich, Germany.
• Peter B. Dixon & Maureen T. Rimmer & Daniel Mason-D'Croz, 2020. "Computable general equilibrium simulations of the effects on the U.S. economy of reductions in beef consumption," Centre of Policy Studies/IMPACT Centre Working Papers g-311, Victoria University, Centre of Policy Studies/IMPACT Centre.
• Fischer, Benjamin & Hügle, Dominik, 2020. "The private and fiscal returns to higher education: A simulation approach for a young German cohort," Discussion Papers 2020/21, Free University Berlin, School of Business & Economics.
• Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
• Rodriguez Castelan, Carlos & Araar, Abdelkrim & Malásquez, Eduardo A. & Ochoa, Rogelio Granguillhome, 2021. "Competition Reform and Household Welfare: A Microsimulation Analysis of the Telecommunication Sector in Ethiopia," IZA Discussion Papers 14044, Institute of Labor Economics (IZA).
• Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
• Le Trung Hieu, 2020. "Deep Reinforcement Learning for Stock Portfolio Optimization," Papers 2012.06325, arXiv.org.
• KONDO Keisuke, 2020. "Simulating the Impacts of Interregional Mobility Restriction on the Spatial Spread of COVID-19 in Japan," Discussion papers 20089, Research Institute of Economy, Trade and Industry (RIETI).
• Hezhi Luo & Yuanyuan Chen & Xianye Zhang & Duan Li & Huixian Wu, 2020. "Effective Algorithms for Optimal Portfolio Deleveraging Problem with Cross Impact," Papers 2012.07368, arXiv.org, revised Jan 2021.
• Andrei Cozma & Christoph Reisinger, 2020. "Simulation of conditional expectations under fast mean-reverting stochastic volatility models," Papers 2012.09726, arXiv.org, revised Oct 2021.
• Hull, Isaiah & Sattath, Or & Diamanti, Eleni & Wendin, Göran, 2020. "Quantum Technology for Economists," Working Paper Series 398, Sveriges Riksbank (Central Bank of Sweden).
• Louis Golowich & Shengwu Li, 2021. "On the Computational Properties of Obviously Strategy-Proof Mechanisms," Papers 2101.05149, arXiv.org, revised Oct 2022.
• Heinrich, Florian & Appel, Franziska & Balmann, Alfons, 2019. "Can land market regulations fulfill their promises?," FORLand Working Papers 12 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
• NARITA Yusuke & AIHARA Shunsuke & SAITO Yuta & MATSUTANI Megumi & YATA Kohei, 2020. "Machine Learning as Natural Experiment: Method and Deployment at Japanese Firms (Japanese)," Discussion Papers (Japanese) 20045, Research Institute of Economy, Trade and Industry (RIETI).
• Böhl, Gregor, 2021. "Efficient solution and computation of models with occasionally binding constraints," IMFS Working Paper Series 148, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Building Cross-Sectional Systematic Strategies By Learning to Rank," Papers 2012.07149, arXiv.org.
• Gabriel Ahlfeldt & Thilo N. H. Albers & Kristian Behrens, 2020. "Prime Locations," CESifo Working Paper Series 8768, CESifo.
• Rodríguez-García, Jair Hissarly & Venegas-Martínez, Francisco, 2021. "Reducción de la brecha del crédito en México en un ambiente de incertidumbre generada por la pandemia COVID-19: Un enfoque de ciencia de datos (machine learning) [Reducing the credit gap in Mexico ," MPRA Paper 105133, University Library of Munich, Germany.
• Daniel Fehrle & Christopher Heiberger & Johannes Huber, 2020. "Polynomial chaos expansion: Efficient evaluation and estimation of computational models," Discussion Paper Series 341, Universitaet Augsburg, Institute for Economics.
• Karim Barigou & Valeria Bignozzi & Andreas Tsanakas, 2021. "Insurance valuation: A two-step generalised regression approach," Working Papers hal-03043244, HAL.
• Gadat, Sébastien & Gavra, Ioana, 2021. "Asymptotic study of stochastic adaptive algorithm in non-convex landscape," TSE Working Papers 21-1175, Toulouse School of Economics (TSE).
• Xavier Warin, 2021. "Deep learning for efficient frontier calculation in finance," Papers 2101.02044, arXiv.org, revised Feb 2022.
• Max Kleinebrahm & Jacopo Torriti & Russell McKenna & Armin Ardone & Wolf Fichtner, 2021. "Using attention to model long-term dependencies in occupancy behavior," Papers 2101.00940, arXiv.org.
• Pumplun, Luisa & Fecho, Mariska & Islam, Nihal & Buxmann, Peter, 2021. "Machine Learning Systems in Clinics – How Mature Is the Adoption Process in Medical Diagnostics?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124660, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Dominick Bartelme & Ting Lan & Andrei A. Levchenko, 2020. "Specialization, Market Access and Real Income," NBER Working Papers 28274, National Bureau of Economic Research, Inc.
• Jeffrey Grogger & Sean Gupta & Ria Ivandic & Tom Kirchmaier, 2020. "Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases," NBER Working Papers 28293, National Bureau of Economic Research, Inc.
• Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Nanbo Peng & Dongdong Wang & Zhixiang Huang, 2020. "The Causal Learning of Retail Delinquency," Papers 2012.09448, arXiv.org.
• Hinrichs, Nils & Kolbe, Jens & Werwatz, Axel, 2020. "AVM and high dimensional data: Do ridge, the lasso or the elastic net provide an "automated" solution?," FORLand Working Papers 22 (2020), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
• Jeremy Fouliard & Michael Howell & Hélène Rey & Vania Stavrakeva, 2020. "Answering the Queen: Machine Learning and Financial Crises," NBER Working Papers 28302, National Bureau of Economic Research, Inc.
• Andrew Bennett & Nathan Kallus, 2020. "The Variational Method of Moments," Papers 2012.09422, arXiv.org, revised Mar 2023.
• Juan Arismendi-Zambrano & Massimo Guidolin & Alessia Paccagnini, 2020. "Federal reserve chair communication sentiments’ heterogeneity, personal characteristics, and their impact on target rate discovery," CAMA Working Papers 2020-105, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Nicola Cortinovis & Frank van der Wouden, 2021. "Better by design? Collaboration and performance in the board-game industry," Papers in Evolutionary Economic Geography (PEEG) 2104, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2021.
• Alexis Marchal, 2020. "Risk & returns around FOMC press conferences: a novel perspective from computer vision," Papers 2012.06573, arXiv.org, revised Jan 2021.
• McNamara, Sarah, 2020. "Returns to higher education and dropouts: A double machine learning approach," ZEW Discussion Papers 20-084, ZEW - Leibniz Centre for European Economic Research.