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Machine learning for econometrics and related topics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for econometrics and related topicsedited by Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka.
other author:	Kreinovich, Vladik.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	ix, 499 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Econometrics.
Online resource:	https://doi.org/10.1007/978-3-031-43601-7
ISBN:	9783031436017$q(electronic bk.)

Machine learning for econometrics and related topics
Machine learning for econometrics and related topics[electronic resource] /edited by Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka. - Cham :Springer Nature Switzerland :2024. - ix, 499 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v. 5082198-4190 ;. - Studies in systems, decision and control ;v.3..

In the last decades, machine learning techniques - especially techniques of deep learning - led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy - and, more generally, issues of fairness and discrimination. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.

ISBN: 9783031436017$q(electronic bk.)

Standard No.: 10.1007/978-3-031-43601-7doiSubjects--Topical Terms:

182271
Econometrics.

LC Class. No.: HB139

Dewey Class. No.: 330.015195

Machine learning for econometrics and related topics
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520 $a In the last decades, machine learning techniques - especially techniques of deep learning - led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy - and, more generally, issues of fairness and discrimination. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.
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