Machine Learning for Econometrics (Paperback)

Christophe Gaillac

ISBN 10: 0198918836 / ISBN 13: 9780198918837

Published by Oxford University Press, Oxford, 2025

About this Item

Paperback. Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as wellas synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics andnumerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques. Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780198918837

Bibliographic Details

• Title: Machine Learning for Econometrics (Paperback)
• Publisher: Oxford University Press, Oxford
• Publication Date: 2025
• Language: English
• ISBN 10: 0198918836
• ISBN 13: 9780198918837
• Binding: Paperback
• Condition: new

About this title

Synopsis

Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data.

The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques.

About the Author

Christophe Gaillac, Associate Professor, University of Geneva,Jérémy L'Hour, Quantitative researcher, Capital Fund Management

Christophe Gaillac is an Associate Professor at the University of Geneva, GSEM. He was a postdoctoral prize research fellow at Oxford University and Nuffield College, and received his PhD in Economics from the Toulouse School of Economics.


Jérémy L'Hour is a quantitative researcher at Capital Fund Management (CFM), a Paris-based systematic hedge fund. He received his PhD in Economics from Université Paris-Saclay.