| 書目名稱 | Econometrics with Machine Learning |
| 編輯 | Felix Chan,László Mátyás |
| 視頻video | http://file.papertrans.cn/302/301474/301474.mp4 |
| 概述 | Presents how machine learning techniques can be applied to empirical econometric problems.Enhances and expands the econometrics toolbox in theory and in practice.Takes a multidisciplinary approach in |
| 叢書名稱 | Advanced Studies in Theoretical and Applied Econometrics |
| 圖書封面 |  |
| 描述 | This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice.?.Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques furtherand make them even more readily applicable in econometrics?.As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in develo |
| 出版日期 | Book 2022 |
| 關(guān)鍵詞 | Machine Learning and causality; Linear models; Non-linear models; Econometric forecasting and predictio |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-15149-1 |
| isbn_softcover | 978-3-031-15151-4 |
| isbn_ebook | 978-3-031-15149-1Series ISSN 1570-5811 Series E-ISSN 2214-7977 |
| issn_series | 1570-5811 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機版|小黑屋|
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