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Econometrics with machine learning

Chan, Felix.

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Econometrics with machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Econometrics with machine learning/ edited by Felix Chan, Laszlo Matyas.
其他作者:	Chan, Felix.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxii, 371 p. :ill., digital ;24 cm.
內容註:	Linear Econometric Models with Machine Learning -- Nonlinear Econometric Models with Machine Learning -- The Use of Machine Learning in Treatment Effect Estimation -- Forecasting with Machine Learning Methods -- Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods -- Econometrics of Networks with Machine Learning -- Fairness in Machine Learning and Econometrics -- Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance -- Poverty, Inequality and Development Studies with Machine Learning -- Machine Learning for Asset Pricing.
Contained By:	Springer Nature eBook
標題:	Econometrics - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-15149-1
ISBN:	9783031151491

Econometrics with machine learning
Econometrics with machine learning[electronic resource] /edited by Felix Chan, Laszlo Matyas. - Cham :Springer International Publishing :2022. - xxii, 371 p. :ill., digital ;24 cm. - Advanced studies in theoretical and applied econometrics,v. 532214-7977 ;. - Advanced studies in theoretical and applied econometrics ;v. 53..

Linear Econometric Models with Machine Learning -- Nonlinear Econometric Models with Machine Learning -- The Use of Machine Learning in Treatment Effect Estimation -- Forecasting with Machine Learning Methods -- Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods -- Econometrics of Networks with Machine Learning -- Fairness in Machine Learning and Econometrics -- Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance -- Poverty, Inequality and Development Studies with Machine Learning -- Machine Learning for Asset Pricing.

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 further and 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 developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.

ISBN: 9783031151491

Standard No.: 10.1007/978-3-031-15149-1doiSubjects--Topical Terms:

710078
Econometrics
--Data processing.

LC Class. No.: HB139 / .E36 2022

Dewey Class. No.: 330.015195

Econometrics with machine learning
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520 $a 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 further and 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 developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
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