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Book 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.

Book details

Book category Books in German Sozialwissenschaften, Recht, Wirtschaft Wirtschaft Betriebswirtschaft

• Full title: Machine Learning for Econometrics
• Author: Jeremy L'Hour
• Language: English
• Binding: Hardback
• Number of pages: 352
• EAN: 9780198918820
• ISBN: 0198918828
• ID: 47057704
• Publisher: Oxford University Press
• Weight: 772 g
• Dimensions: 246 × 171 mm
• Date of publishing: 22. June 2025

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