Description

Chapter 10

These slides accompany Chapter 10 (“Selected Machine Learning Tools for Econometrics in R”) of the book

Yongmiao Hong, Oliver Linton, Jiajing Sun
Econometrics and Time Series Methods: Theory, Applications, and R Implementation.

The slides provide an applied introduction to machine learning methods in an econometric context, with a focus on tools that complement and extend classical parametric modeling. They typically cover topics such as regularized regression (e.g., ridge and lasso), tree-based methods, model selection and cross-validation, and prediction-focused evaluation criteria. The emphasis is on how these tools can be used for forecasting, dimension reduction, and flexible approximation of nonlinear relationships, while respecting the goals and constraints of empirical economics and finance.

Consistent with the book’s integration of theory, applications, and computation, the slides are closely tied to implementation in R. They illustrate how to fit machine learning models using standard R packages, tune hyperparameters via cross-validation, compare out-of-sample performance, and interpret outputs in an econometric workflow. The material is suitable for advanced undergraduate and graduate teaching and can be directly used or adapted by instructors for lectures, labs, and self-study.

Unless otherwise indicated, the slides are shared under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0). Readers and instructors who wish to request the LaTeX source files or provide feedback are welcome to contact us at jiajing.sun@gmail.com.