Overview
- Presents how machine learning techniques can be applied to empirical econometric problems
- Enhances and expands the econometrics toolbox in theory and in practice
- Takes a multidisciplinary approach in developing the disciplines of machine learning and econometrics in conjunction
Part of the book series: Advanced Studies in Theoretical and Applied Econometrics (ASTA, volume 53)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
- Machine Learning and causality
- Linear models
- Non-linear models
- Econometric forecasting and prediction
- Policy evaluation
- Network data
- Poverty
- Inequality
- Machine learning in Finance
- Empirical applications
- Testing statistical hypotheses
- Big data
- Econometric techniques
- Modelling macroeconomic relations
- Discrete Choice models
About this book
Throughout the volume, the authors raise and answer six questions: 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 is the prediction 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 furtherand make them even more readily applicable in econometrics?
As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Editors and Affiliations
About the editors
Felix Chan is an Associate Professor at Curtin University and an elected Fellow of the Modelling and Simulation Society of Australia and New Zealand (MSSANZ). He serves as the Deputy Head, School of Accounting, Economics and Finance and was the Director of Centre for Research in Applied Economics (CRAE) between 2017 and 2022.Associate Professor Chan had also served as an external consultant to the Commonwealth Grant Commission (CGC), Department of Treasury Western Australia and Chamber of Commerce and Industry (WA) on issues surrounding forecasting, data analytics and mathematical modelling.
Bibliographic Information
Book Title: Econometrics with Machine Learning
Editors: Felix Chan, László Mátyás
Series Title: Advanced Studies in Theoretical and Applied Econometrics
DOI: https://doi.org/10.1007/978-3-031-15149-1
Publisher: Springer Cham
eBook Packages: Economics and Finance, Economics and Finance (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-15148-4Published: 08 September 2022
Softcover ISBN: 978-3-031-15151-4Published: 09 September 2023
eBook ISBN: 978-3-031-15149-1Published: 07 September 2022
Series ISSN: 1570-5811
Series E-ISSN: 2214-7977
Edition Number: 1
Number of Pages: XXII, 371
Number of Illustrations: 13 b/w illustrations, 36 illustrations in colour
Topics: Econometrics, Machine Learning, Economic Theory/Quantitative Economics/Mathematical Methods, Macroeconomics/Monetary Economics//Financial Economics