Econometrics with Machine Learning

This book discusses the use of machine learning tools and techniques in econometrics, highlighting their similarities, potential applications, and potential extensions. It takes a multidisciplinary approach, combining machine learning and econometrics, and is a valuable resource for scholars, researchers, students, policy-makers, and practitioners.

Format: Hardback
Length: 371 pages
Publication date: 08 September 2022
Publisher: Springer International Publishing AG

This comprehensive book serves as a valuable resource for economists, econometricians, and practitioners seeking to harness the power of machine learning tools and techniques in their research endeavors. It delves into the intricate relationship between existing econometric methods and machine learning, exploring how these two fields can be seamlessly integrated to enhance and expand the econometrics toolbox.

The authors raise and address six critical questions throughout the volume:

1. What are the similarities between existing econometric and machine learning techniques?
2. To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable are the predictions from machine learning algorithms given the ever-changing nature of human behavior?
3. Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in 'big data'?
4. How can existing econometric techniques be extended by incorporating machine learning concepts?
5. How can new econometric tools and approaches be elaborated on based on machine learning techniques?
6. Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics?

In response to these questions, the book takes a multidisciplinary approach, developing both machine learning and econometrics in conjunction rather than in isolation. It emphasizes the importance of understanding the underlying principles and methodologies of both fields to achieve successful integration.

The book begins by providing an overview of machine learning, including its historical development, fundamental concepts, and applications in various fields. It then delves into the econometric aspects of machine learning, discussing how these techniques can be used to improve the accuracy of economic models, analyze large datasets, and identify patterns and relationships that may be difficult to detect using traditional econometric methods.

Throughout the volume, the authors employ real-world examples and case studies to illustrate the practical applications of machine learning in econometrics. They showcase how machine learning algorithms can be used to analyze financial data, predict economic outcomes, and develop new forecasting models. The book also discusses the challenges and limitations of using machine learning in econometrics, such as the need for robust data, the potential for overfitting, and the ethical considerations associated with data privacy and bias.

In conclusion, this book is a must-read for scholars, researchers, students, policy-makers, and practitioners who are using econometrics in theory or in practice. It provides a comprehensive and up-to-date introduction to machine learning tools and techniques, and it offers practical insights and guidance on how these can be applied to enhance and expand the econometrics toolbox. By integrating machine learning with econometric methods, researchers can gain a deeper understanding of economic phenomena, develop more accurate models, and make more informed policy decisions.

Weight: 748g
Dimension: 164 x 242 x 31 (mm)
ISBN-13: 9783031151484
Edition number: 1st ed. 2022