This 20-minute video by SAS’ Xilong Chen, designed for all levels of audience, helps you understand how to use the VARMAX procedure to simultaneously analyze multiple time series.
To understand the past, update the present, and forecast the future of a time series, you must often use information from other time series. This is why simultaneously modeling multiple time series plays a critical role in many fields. Xilong’s video shows how easy it is to use the VARMAX procedure to estimate and interpret several popular and powerful multivariate time series models, including the vector autoregressive (VAR) model, the vector error correction model (VECM), and the multivariate GARCH model. Simple examples illustrate Granger causality tests for identifying predictive causality, impulse response analysis for finding the effect of shocks, cointegration and its importance in forecasting, model selection for dealing with the trade-off between bias and variance, and volatility forecasting for risk management and portfolio optimization.
Video highlights
00:26 – Outline
01:05 – Vector Autoregression (VAR)
07:51 – Vector Error Correction Model (VECM)
12:22 – Multivariate GARCH Model
Related Resources
Xilong’s SASGF paper on the topic (proceedings)
SAS Code for the paper at GitHub (code)
Multivariate Time Series Analysis with the VARMAX Procedure (video)
