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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. 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:
- Tae-Hwy Lee & Jianghao Chu & Aman Ullah, 2018, "Component-wise AdaBoost Algorithms for High-dimensional Binary Classi fication and Class Probability Prediction," Working Papers, University of California at Riverside, Department of Economics, number 201907, Jul.
- Domenico Delli Gatti & Jakob Grazzini, 2019, "Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models," CESifo Working Paper Series, CESifo, number 7894.
- 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, Swiss Finance Institute, number 19-54, Oct.
- 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, Verein für Socialpolitik / German Economic Association, number 203503.
- Julia M. Puaschunder, 2019, "Towards Legal Empirical Macrodynamics: A Research Agenda," Proceedings of the 14th International RAIS Conference, August 19-20, 2019, Research Association for Interdisciplinary Studies, number 010JP, Aug.
- 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, Verein für Socialpolitik / German Economic Association, number 203499.
- Vladimir Puzyrev, 2019, "Deep convolutional autoencoder for cryptocurrency market analysis," Papers, arXiv.org, number 1910.12281, Oct.
- Giuseppe Carlo Calafiore & Marisa Hillary Morales & Vittorio Tiozzo & Serge Marquie, 2019, "A Classifiers Voting Model for Exit Prediction of Privately Held Companies," Papers, arXiv.org, number 1910.13969, Oct.
- 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, International Institute of Social and Economic Sciences, number 9110624, Jul.