- ホーム
- > 洋書
- > 英文書
- > Business / Economics
Full 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.
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
