Machine Learning for Econometrics

Inbunden, Engelska, 2025

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

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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.

Produktinformation

Utgivningsdatum2025-06-06
Mått175 x 253 x 26 mm
Vikt772 g
FormatInbunden
SpråkEngelska
Antal sidor352
FörlagOUP OXFORD
ISBN9780198918820

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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.

1: IntroductionPart I. Statistics and Econometrics Prerequisites2: Statistical tools3: Causal inferencePart II. High-dimension and variable selection4: Post-selection inference5: Generalization and methodology6: High dimension and endogeneity7: Going furtherPart III. Treatment effect heterogeneity8: Inference on heterogeneous effects9: Optimal policy learningPart IV. Aggregated data and macroeconomic forecasting10: The synthetic control method11: Forecasting in high-dimensionPart V. Textual data12: Working with text data13: Word embeddings14: Modern language modelsPart VI. Exercises15: ExercisesBibliographyIndex