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Titlebook: Econometrics with Machine Learning; Felix Chan,László Mátyás Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive li

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发表于 2025-3-21 19:43:36 | 显示全部楼层 |阅读模式
书目名称Econometrics with Machine Learning
编辑Felix Chan,László Mátyás
视频video
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
丛书名称Advanced Studies in Theoretical and Applied Econometrics
图书封面Titlebook: Econometrics with Machine Learning;  Felix Chan,László Mátyás Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive li
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
关键词Machine Learning and causality; Linear models; Non-linear models; Econometric forecasting and predictio
版次1
doihttps://doi.org/10.1007/978-3-031-15149-1
isbn_softcover978-3-031-15151-4
isbn_ebook978-3-031-15149-1Series 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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Marion A. Hersh,Michael A. Johnsoniscrete outcome, problems. Overall, the chapter attempts to identify the nexus between these ML methods and conventional techniques ubiquitously used in applied econometrics. This includes a discussion of the advantages and disadvantages of each approach. Several benefits, as well as strong connecti
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Non-Coding RNA Function and Structure,. Epidemiologists have generally approached such problems using propensity score matching or inverse probability treatment weighting within a potential outcomes framework. This approach still focuses on the estimation of a parameter in a structural model. A more recent method, known as doubly robust
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,Log-Arithmetic, with Single and Dual Base,y projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space.We also acknowledge that policymakers may incur costs when moving away from the status quo. . is thus introduced as an intermediate se
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The Use of Machine Learning in Treatment Effect Estimation,
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1570-5811 ven 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 develo978-3-031-15151-4978-3-031-15149-1Series ISSN 1570-5811 Series E-ISSN 2214-7977
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