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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.
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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.
"About this title" may belong to another edition of this title.
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MACHINE LEARNING FOR ECONOMETRICS
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Machine Learning for Econometrics
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Machine Learning for Econometrics
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Oxford University Press OUP, 2025
ISBN 10: 0198918836
ISBN 13: 9780198918837
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Machine Learning for Econometrics
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Machine Learning for Econometrics
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Machine Learning for Econometrics
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Machine Learning for Econometrics
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Machine Learning for Econometrics
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MACHINE LEARNING FOR ECONOMETRIC P (eng)
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Machine Learning for Econometrics
Published by
Oxford University Press, GB, 2025
ISBN 10: 0198918836
ISBN 13: 9780198918837
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Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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Paperback. Condition: New. 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. Seller Inventory # LU-9780198918837
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