Machine Learning for Econometrics and Related Topics
Machine Learning for Econometrics and Related Topics
Buch
- Herausgeber: Vladik Kreinovich, Woraphon Yamaka, Songsak Sriboonchitta
- Springer Nature Switzerland, 06/2024
- Einband: Gebunden, HC runder Rücken kaschiert
- Sprache: Englisch
- ISBN-13: 9783031436000
- Bestellnummer: 11887198
- Umfang: 512 Seiten
- Auflage: 2024
- Gewicht: 1020 g
- Maße: 241 x 160 mm
- Stärke: 26 mm
- Erscheinungstermin: 2.6.2024
- Serie: Studies in Systems, Decision and Control - Band 508
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
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.