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Chapter 3: Vector Autoregressive Methods
Previously, we have introduced the classical approaches in forecasting single/univariate time series like the Autoregressive-Moving-Average (ARIMA) model and the simple linear regression model. We learned that stationarity is a condition that is necessary when using ARIMA while this need not be imposed when using the linear regression model. In this notebook, we extend the forecasting problem to a more generalized framework where we deal with multivariate time series–time series which has more than one time-dependent variable. More specifically, we introduce vector autoregressive (VAR) models and show how they can be used in forecasting mutivariate time series.
The notebook is outlined as follows: * Multivariate Time Series model * Motivation * Univariate VS Multivariate Time Series * Examples * Foundations * Vector Autoregressive (VAR) Models * VAR(1) model * VAR(p) model * Choosing the order p * Building a VAR model * Structural Analysis * Impulse Response Function * Forecast Error Variance Decomposition * Takeaways * References