Machine Learning for Econometrics and Related Topics

Author:   Vladik Kreinovich ,  Songsak Sriboonchitta ,  Woraphon Yamaka
Publisher:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   508
ISBN:  

9783031436000


Pages:   499
Publication Date:   02 June 2024
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Machine Learning for Econometrics and Related Topics


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Overview

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.

Full Product Details

Author:   Vladik Kreinovich ,  Songsak Sriboonchitta ,  Woraphon Yamaka
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   508
ISBN:  

9783031436000


ISBN 10:   3031436008
Pages:   499
Publication Date:   02 June 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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