Machine Learning for Econometrics
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ISBN:
9780198918837
Publication date:
11/09/2025
Paperback
352 pages
246x171mm
Price: 1495.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780198918837
Publication date:
11/09/2025
Paperback
352 pages
Christophe Gaillac & Jérémy L'Hour
- Bridges the gap between econometric methods and modern ML techniques, providing a comprehensive understanding of how ML tools can be used in econometrics
- Emphasizes the predictive capabilities of machine learning, while also addressing how these methods can be used to infer causal relationships from data with greater credibility
- Provides a thorough theoretical treatment of machine learning methods and their application in economics and econometrics
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Economists analyze data. Machine learning (ML) offers a powerful set of tools for doing just that. But while econometrics and ML share a foundation in statistics, their aims and philosophies often diverge.
Posted on July 21, 2025
Rights: OUP UK (INDIAN TERRITORY)
Christophe Gaillac & Jérémy L'Hour
Description
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 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.
Christophe Gaillac & Jérémy L'Hour
Table of contents
1:Introduction
Part I. Statistics and Econometrics Prerequisites
2:Statistical tools
3:Causal inference
Part II. High-dimension and variable selection
4:Post-selection inference
5:Generalization and methodology
6:High dimension and endogeneity
7:Going further
Part III. Treatment effect heterogeneity
8:Inference on heterogeneous effects
9:Optimal policy learning
Part IV. Aggregated data and macroeconomic forecasting
10:The synthetic control method
11:Forecasting in high-dimension
Part V. Textual data
12:Working with text data
13:Word embeddings
14:Modern language models
Part VI. Exercises
15:Exercises
Bibliography
Index
Christophe Gaillac & Jérémy L'Hour
Description
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 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.
Table of contents
1:Introduction
Part I. Statistics and Econometrics Prerequisites
2:Statistical tools
3:Causal inference
Part II. High-dimension and variable selection
4:Post-selection inference
5:Generalization and methodology
6:High dimension and endogeneity
7:Going further
Part III. Treatment effect heterogeneity
8:Inference on heterogeneous effects
9:Optimal policy learning
Part IV. Aggregated data and macroeconomic forecasting
10:The synthetic control method
11:Forecasting in high-dimension
Part V. Textual data
12:Working with text data
13:Word embeddings
14:Modern language models
Part VI. Exercises
15:Exercises
Bibliography
Index
