Today I learnt more on Vector Auto Regression
- Vector Autoregression (VAR) is a forecasting algorithm that can be used when two or more time series influence each other. That is, the relationship between the time series involved is bi-directional.
- VAR modeling is a multi-step process, and a complete VAR analysis involves:
1) Specifying and estimating a VAR model.
2) Using inferences to check and revise the model (as needed).
3) Forecasting.
4) Structural analysis. - In a VAR model, each variable is modeled as a linear function of past lags of itself and past lags of other variables in the system.
- VAR models differ from univariate autoregressive models because they allow feedback to occur between the variables in the model.
- An estimated VAR model can be used for forecasting, and the quality of the forecasts can be judged, in ways that are completely analogous to the methods used in univariate autoregressive modelling.
- Using an autoregressive (AR) modeling approach, the vector autoregression (VAR) method examines the relationships between multiple time series variables at different time steps.
- The VAR model’s parameter specification involves providing the order of the AR(p) model, which represents the number of lagged values included in the analysis.
- By applying this technique to mutually independent time series, the VAR method offers a useful tool for investigating their interdependencies without accounting for overall pattern influences.