Machine Learning for Econometrics and Related Topics (Studies in Systems, Decision and Control, 508)
by 作者: Vladik Kreinovich (Editor), Songsak Sriboonchitta (Editor), Woraphon Yamaka (Editor)
Publisher Finelybook 出版社: Springer
Edition 版本: 2024th
Publication Date 出版日期: 2024-06-02
Language 语言: English
Pages 页数: 508 pages
ISBN-10 书号: 3031436008
ISBN-13 书号: 9783031436000
Book Description
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.
From the Back Cover
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.
