Buch, Englisch, 354 Seiten, Format (B × H): 174 mm x 248 mm, Gewicht: 776 g
Buch, Englisch, 354 Seiten, Format (B × H): 174 mm x 248 mm, Gewicht: 776 g
ISBN: 978-0-19-891882-0
Verlag: Oxford University Press
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
- 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
