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Report NEP-CMP-2020-06-15
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:
- Michael Puglia & Adam Tucker, 2020, "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-038, May, DOI: 10.17016/FEDS.2020.038.
- Item repec:hal:wpaper:hal-02619589 is not listed on IDEAS anymore
- Maake, Witness & Van Zyl, Terence, 2020, "Applications of Machine Learning to Estimating the Sizes and Market Impact of Hidden Orders in the BRICS Financial Markets," MPRA Paper, University Library of Munich, Germany, number 99075, Feb.
- Shiro Takeda & Toshi H. Arimura, 2020, "A Computable General Equilibrium Analysis of Environmental Tax Reform in Japan," RIEEM Discussion Paper Series, Research Institute for Environmental Economics and Management, Waseda University, number 2002, Jun.
- Yue Qiu & Tian Xie & Jun Yu, 2020, "Forecast combinations in machine learning," Economics and Statistics Working Papers, Singapore Management University, School of Economics, number 13-2020, May.
- Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2020, "Making text count: economic forecasting using newspaper text," Bank of England working papers, Bank of England, number 865, May.
- Marcin Chlebus & Zuzanna Osika, 2020, "Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-15.
- Giuseppe Attanasi & Samuele Centorrino & Elena Manzoni, 2020, "Zero-Intelligence vs. Human Agents: An Experimental Analysis of the Efficiency of Double Auctions and Over-the-Counter Markets of Varying Sizes," Working Papers, University of Verona, Department of Economics, number 05/2020, Mar.
- Hess Chung & Etienne Gagnon & Taisuke Nakata & Matthias Paustian & Bernd Schlusche & James Trevino & Diego Vilán & Wei Zheng, 2020, "Monetary Policy Options at the Effective Lower Bound: Assessing the Federal Reserve’s Current Policy Toolkit," CARF F-Series, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, number CARF-F-483, May.
- Schönheit, David & Hladik, Dirk & Hobbie, Hannes & Möst, Dominik, 2020, "ELMOD documentation: Modeling of flow-based market coupling and congestion management," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 217278.
- Diego d’Andria & Jason DeBacker & Richard W. Evans & Jonathan Pycroft & Wouter van der Wielen & Magdalena Zachlod-Jelec, 2020, "EDGE-M3: A Dynamic General Equilibrium Micro-Macro Model for the EU Member States," JRC Working Papers on Taxation & Structural Reforms, Joint Research Centre, number 2020-03, May.
- Jarmulska, Barbara, 2020, "Random forest versus logit models: which offers better early warning of fiscal stress?," Working Paper Series, European Central Bank, number 2408, May.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020, "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers, arXiv.org, number 2005.14057, May, revised Dec 2020.
- Fabio Antonelli & Alessandro Ramponi & Sergio Scarlatti, 2020, "A moment matching method for option pricing under stochastic interest rates," Papers, arXiv.org, number 2005.14063, May.
- Escribano, Álvaro & Wang, Dandan, 2020, "Forecasting gasoline prices with mixed random forest error correction models," UC3M Working papers. Economics, Universidad Carlos III de Madrid. Departamento de EconomÃa, number 30557, Jun.
- Subhadeep & Mukhopadhyay & Kaijun Wang, 2020, "Breiman's "Two Cultures" Revisited and Reconciled," Papers, arXiv.org, number 2005.13596, May.
- Zihao Zhang & Stefan Zohren & Stephen Roberts, 2020, "Deep Learning for Portfolio Optimization," Papers, arXiv.org, number 2005.13665, May, revised Jan 2021.
- Michael Creel, 2020, "Inference Using Simulated Neural Moments," Working Papers, Barcelona School of Economics, number 1182, Jun.
- Ian M. Trotter & Lu'is A. C. Schmidt & Bruno C. M. Pinto & Andrezza L. Batista & J'essica Pellenz & Maritza Isidro & Aline Rodrigues & Attawan G. S. Suela & Loredany Rodrigues, 2020, "COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model," Papers, arXiv.org, number 2005.13722, May, revised Jun 2020.
- Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020, "Using Machine Learning to Forecast Future Earnings," Papers, arXiv.org, number 2005.13995, May.
- Jan H van Heerden, 2020, "The Possible Effects of the Extended Lockdown Period on the South African Economy: A CGE Analysis," Working Papers, University of Pretoria, Department of Economics, number 202042, May.
- Jaqueson K. Galimberti, 2020, "Information Weighting Under Least Squares Learning," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2020-46, May.
- Christian Dehm & Thai Nguyen & Mitja Stadje, 2020, "Non-concave expected utility optimization with uncertain time horizon," Papers, arXiv.org, number 2005.13831, May, revised Oct 2021.