Report NEP-CMP-2019-11-04

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:

• Tae-Hwy Lee & Jianghao Chu & Aman Ullah, 2018. "Component-wise AdaBoost Algorithms for High-dimensional Binary Classi fication and Class Probability Prediction," Working Papers 201907, University of California at Riverside, Department of Economics.
• Domenico Delli Gatti & Jakob Grazzini, 2019. "Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models," CESifo Working Paper Series 7894, CESifo.
• Damir Filipović & Kathrin Glau & Yuji Nakatsukasa & Francesco Statti, 2019. "Weighted Monte Carlo with Least Squares and Randomized Extended Kaczmarz for Option Pricing," Swiss Finance Institute Research Paper Series 19-54, Swiss Finance Institute.
• Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.
• Julia M. Puaschunder, 2019. "Towards Legal Empirical Macrodynamics: A Research Agenda," Proceedings of the 14th International RAIS Conference, August 19-20, 2019 010JP, Research Association for Interdisciplinary Studies.
• Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
• Vladimir Puzyrev, 2019. "Deep convolutional autoencoder for cryptocurrency market analysis," Papers 1910.12281, arXiv.org.
• Giuseppe Carlo Calafiore & Marisa Hillary Morales & Vittorio Tiozzo & Serge Marquie, 2019. "A Classifiers Voting Model for Exit Prediction of Privately Held Companies," Papers 1910.13969, arXiv.org.
• Yvette Burton, 2019. "Keeping Real World Bias Out of Artificial Intelligence ?Examination of Coder Bias in Data Science Recruitment Solutions?," Proceedings of International Academic Conferences 9110624, International Institute of Social and Economic Sciences.