Description

Chapter 3

These slides accompany Chapter 3 (“Multivariate Linear 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 structured introduction to multivariate linear time series analysis, focusing on how multiple time series evolve jointly over time. They build on the univariate theory to develop key concepts and tools for analyzing dynamic interactions among several variables. Core topics typically include vector-valued stochastic processes, second-order properties of multivariate time series, vector autoregressive (VAR) and vector moving average (VMA) models, and their VARMA representations. The slides also discuss issues such as model specification, stability and stationarity conditions, impulse–response analysis, and forecasting in a multivariate setting.

Consistent with the book’s emphasis on integrating theory, applications, and computation, the slides link the underlying econometric theory to practical implementation in R. They illustrate how to estimate VAR-type models, interpret dynamic relationships (including lead–lag structure and Granger-type predictive relationships), and evaluate model fit and forecast performance in empirical applications. The material is suitable for advanced undergraduate and graduate courses 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.