Report NEP-CMP-2022-10-17

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:

• Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
• Saeed Nosratabadi & Roya Khayer Zahed & Vadim Vitalievich Ponkratov & Evgeniy Vyacheslavovich Kostyrin, 2022. "Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review," Papers 2209.07335, arXiv.org.
• Yang Liu & Di Yang & Mr. Yunhui Zhao, 2022. "Housing Boom and Headline Inflation: Insights from Machine Learning," IMF Working Papers 2022/151, International Monetary Fund.
• Roberto Baviera & Pietro Manzoni, 2022. "Tree-Based Learning in RNNs for Power Consumption Forecasting," Papers 2209.01378, arXiv.org.
• Paul Trust & Ahmed Zahran & Rosane Minghim, 2022. "Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty," Papers 2209.05383, arXiv.org, revised Sep 2022.
• Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2022. "Computing XVA for American basket derivatives by Machine Learning techniques," Papers 2209.06485, arXiv.org.
• Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org, revised Apr 2024.
• Ricardo Muller & Marco Schreyer & Timur Sattarov & Damian Borth, 2022. "RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations," Papers 2209.09157, arXiv.org.
• Emanuel Kohlscheen, 2022. "What does machine learning say about the drivers of inflation?," Papers 2208.14653, arXiv.org, revised Jan 2023.
• Ms. Burcu Hacibedel & Ritong Qu, 2022. "Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach," IMF Working Papers 2022/153, International Monetary Fund.
• Dangxing Chen, 2022. "Two-stage Modeling for Prediction with Confidence," Papers 2209.08848, arXiv.org.
• Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
• David Karpa & Torben Klarl & Michael Rochlitz, 2021. "Artificial Intelligence, Surveillance, and Big Data," Bremen Papers on Economics & Innovation 2108, University of Bremen, Faculty of Business Studies and Economics.
• D Barrera & S Cr'epey & E Gobet & Hoang-Dung Nguyen & B Saadeddine, 2022. "Statistical Learning of Value-at-Risk and Expected Shortfall," Papers 2209.06476, arXiv.org, revised Sep 2024.
• Yukang Jiang & Xueqin Wang & Zhixi Xiong & Haisheng Yang & Ting Tian, 2022. "Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model," Papers 2209.05998, arXiv.org.
• Susan Athey & Dean Karlan & Emil Palikot & Yuan Yuan, 2022. "Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces," Papers 2209.01235, arXiv.org, revised Mar 2023.
• Hugo Inzirillo & Ludovic De Villelongue, 2022. "An Attention Free Long Short-Term Memory for Time Series Forecasting," Papers 2209.09548, arXiv.org.
• Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.
• Hannes Mueller & Christopher Rauh & Alessandro Ruggieri, 2022. "Dynamic Early Warning and Action Model," Working Papers 1355, Barcelona School of Economics.
• Zegners, Dainis & Sunde, Uwe & Strittmatter, Anthony, 2020. "Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach," Rationality and Competition Discussion Paper Series 263, CRC TRR 190 Rationality and Competition.
• Nerijus Cerniauskas & Denisa Sologon & Cathal O'Donoghue & Linas Tarasonis, 2021. "Income inequality and redistribution in Lithuania: The role of policy, labor market, income, and demographics," GRAPE Working Papers 60, GRAPE Group for Research in Applied Economics.
• Ovielt Baltodano L'opez & Roberto Casarin, 2022. "A Dynamic Stochastic Block Model for Multi-Layer Networks," Papers 2209.09354, arXiv.org.
• Zonghui Yao & Dunia López-Pintado & Sara López-Pintado, 2022. "Uncertainty analysis of contagion processes based on a functional approach," Working Papers 22.12, Universidad Pablo de Olavide, Department of Economics.
• Antonio Cutanda & Juan A. Sanchis, 2022. "Labour supply responses to income tax changes in Spain," Working Papers 2207, Department of Applied Economics II, Universidad de Valencia.