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Titlebook: The Econometrics of Multi-dimensional Panels; Theory and Applicati Laszlo Matyas Textbook 2024Latest edition The Editor(s) (if applicable)

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書目名稱The Econometrics of Multi-dimensional Panels
副標(biāo)題Theory and Applicati
編輯Laszlo Matyas
視頻videohttp://file.papertrans.cn/909/908114/908114.mp4
概述2nd revised and extended edition.Presents the econometric foundations and applications of multi-dimensional panels.Useful as a standard reference, textbook, and resource for professionals conducting e
叢書名稱Advanced Studies in Theoretical and Applied Econometrics
圖書封面Titlebook: The Econometrics of Multi-dimensional Panels; Theory and Applicati Laszlo Matyas Textbook 2024Latest edition The Editor(s) (if applicable)
描述.This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis. In light of the big data revolution and the emergence of higher dimensional panel data sets, it provides new results to synthesize existing knowledge on the field. The first, theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions..This second extended and revised edition provides an update of all existent chapters?to reflect on new developments in the area?as well as several new chapters on topics such as machine learning, nonparametric models,networks, and multi-dimensional panels in health economics. The book serves as a standard reference work, a textbook for graduate students in economics, and a source of background material for professionals conducting
出版日期Textbook 2024Latest edition
關(guān)鍵詞Panel data; Multi-dimensional data; Multi-dimensional panels; Econometric modeling; Big Data; Estimation;
版次2
doihttps://doi.org/10.1007/978-3-031-49849-7
isbn_softcover978-3-031-49851-0
isbn_ebook978-3-031-49849-7Series ISSN 1570-5811 Series E-ISSN 2214-7977
issn_series 1570-5811
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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