Machine Learning for Econometrics and Related Topics
Language: English
Published by Springer Nature, 2024
- Hardcover
- New

Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
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Add to basketItem description from seller
508 pages. 9.25x6.10x9.49 inches. In Stock.
Seller Inventory # x-3031436008
- Title
- Machine Learning for Econometrics and Related Topics
- Author
- Kreinovich, Vladik (Editor)/ Sriboonchitta, Songsak (Editor)/ Yamaka, Woraphon (Editor)
- Publisher
- Springer Nature
- Publication year
- 2024
- Condition
- Brand New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3031436008
- ISBN 13
- 9783031436000
- Item weight
- 1.01 kilograms
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."Synopsis" may belong to another edition of this title.
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."About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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