Wow! eBook
Machine Learning for Econometrics and Related Topics
Author(s): Vladik Kreinovich (Editor), Songsak Sriboonchitta (Editor), Woraphon Yamaka (Editor)
- Publisher: Springer
- Publication Date: 2 Jun. 2025
- Language: English
- Print length: 508 pages
- ISBN-10: 3031436032
- ISBN-13: 9783031436031
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.
Editorial Reviews
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.
