Description

These slides accompany Chapter 1 (“Regression Models”) of the book

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

The slides provide a structured introduction to regression models, suitable for advanced undergraduate and graduate courses in econometrics and time series. They outline the econometric motivation for regression analysis, introduce the classical linear regression framework, and develop the theory of estimation and inference for the ordinary least squares (OLS) estimator. Core topics include model specification, interpretation of coefficients, Gauss–Markov assumptions, finite-sample and asymptotic properties of estimators, hypothesis testing, and goodness-of-fit measures.

In addition to the theoretical foundations, the slides emphasize applications and implementation. They illustrate how to estimate regression models, interpret empirical results, and carry out inference using R, aligning with the book’s focus on integrating theory, methods, and computation. The material is designed both for self-study and for classroom instruction, and can be directly used or adapted by instructors.

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.