Description

Chapter 2

These slides accompany Chapter 2 (“Univariate Time Series”) of the book

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

The slides provide a systematic introduction to univariate time series analysis, suitable for advanced undergraduate and graduate courses in econometrics and time series. They review key concepts such as time series data structures, stochastic processes, and different forms of dependence over time. Core topics typically include stationarity, autocovariance and autocorrelation functions, linear time series models (such as AR, MA, and ARMA models), and basic tools for model identification, estimation, and diagnostic checking.

In line with the book’s emphasis on integrating theory, applications, and computation, the slides connect the underlying probabilistic and econometric theory to practical implementation in R. They illustrate how to fit univariate time series models, interpret estimated parameters, conduct forecasting, and assess model adequacy in empirical work. The material is designed to support both self-study and classroom teaching 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.