Report NEP-BIG-2022-10-17

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

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, CPB Netherlands Bureau for Economic Policy Analysis, number 441, Oct, DOI: 10.34932/01mq-sn15.
• David Karpa & Torben Klarl & Michael Rochlitz, 2021, "Artificial Intelligence, Surveillance, and Big Data," Bremen Papers on Economics & Innovation, University of Bremen, Faculty of Business Studies and Economics, number 2108, Nov, DOI: https://doi.org/10.26092/elib/1168.
• Paul Trust & Ahmed Zahran & Rosane Minghim, 2022, "Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty," Papers, arXiv.org, number 2209.05383, Aug, revised Sep 2022.
• Yang Liu & Di Yang & Mr. Yunhui Zhao, 2022, "Housing Boom and Headline Inflation: Insights from Machine Learning," IMF Working Papers, International Monetary Fund, number 2022/151, Jul.
• Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022, "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers, arXiv.org, number 2209.09649, Sep, revised Jul 2023.
• Saeed Nosratabadi & Roya Khayer Zahed & Vadim Vitalievich Ponkratov & Evgeniy Vyacheslavovich Kostyrin, 2022, "Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review," Papers, arXiv.org, number 2209.07335, Sep.
• Emanuel Kohlscheen, 2022, "What does machine learning say about the drivers of inflation?," Papers, arXiv.org, number 2208.14653, Aug, revised Jan 2023.
• Ms. Burcu Hacibedel & Ritong Qu, 2022, "Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach," IMF Working Papers, International Monetary Fund, number 2022/153, Jul.
• Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022, "The boosted HP filter is more general than you might think," Papers, arXiv.org, number 2209.09810, Sep, revised Apr 2024.
• Alessandro Ruggieri & Hannes Mueller, 2022, "Dynamic Early Warning and Action Model," Working Papers, Barcelona School of Economics, number 1355, Jun.
• 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, arXiv.org, number 2209.05998, Jul.
• Omoniyi Alimi & Geua Boe-Gibson & John Gibson, 2022, "Noisy Night Lights Data: Effects on Research Findings for Developing Countries," Working Papers in Economics, University of Waikato, number 22/12, Sep.
• Ricardo Muller & Marco Schreyer & Timur Sattarov & Damian Borth, 2022, "RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations," Papers, arXiv.org, number 2209.09157, Sep.
• Roberto Baviera & Pietro Manzoni, 2022, "RNN(p) for Power Consumption Forecasting," Papers, arXiv.org, number 2209.01378, Sep, revised Nov 2025.
• Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2022, "Computing XVA for American basket derivatives by Machine Learning techniques," Papers, arXiv.org, number 2209.06485, Sep.
• Clara Krämer & Sandrine Cazes, 2022, "Shaping the transition: Artificial intelligence and social dialogue," OECD Social, Employment and Migration Working Papers, OECD Publishing, number 279, Oct, DOI: 10.1787/f097c48a-en.
• Hugo Inzirillo & Ludovic De Villelongue, 2022, "An Attention Free Long Short-Term Memory for Time Series Forecasting," Papers, arXiv.org, number 2209.09548, Sep.
• Dangxing Chen, 2022, "Two-stage Modeling for Prediction with Confidence," Papers, arXiv.org, number 2209.08848, Sep.
• Item repec:crm:wpaper:2322 is not listed on IDEAS anymore

• Jessica Birkholz, 2021, "Do not judge a business idea by its cover: The relation between topics in business ideas and incorporation probability," Bremen Papers on Economics & Innovation, University of Bremen, Faculty of Business Studies and Economics, number 2109, Nov, DOI: https://doi.org/10.26092/elib/1283.
• Michiel Bijlsma & Carin van der Cruijsen & Nicole Jonker, 2021, "Not all data are created equal - Data sharing and privacy," Working Papers, DNB, number 728, Nov.
• Jessica Birkholz & Jutta Günther & Mariia Shkolnykova, 2021, "Using Topic Modeling in Innovation Studies: The Case of a Small Innovation System under Conditions of Pandemic Related Change," Bremen Papers on Economics & Innovation, University of Bremen, Faculty of Business Studies and Economics, number 2101, Jan, DOI: https://doi.org/10.26092/elib/451.
• Susan Athey & Dean Karlan & Emil Palikot & Yuan Yuan, 2022, "Smiles in Profiles: Improving Efficiency While Reducing Disparities in Online Marketplaces," Papers, arXiv.org, number 2209.01235, Sep, revised Mar 2025.

Report NEP-BIG-2022-10-17

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: