VAR | R Documentation |
Searches through the vector of lag orders to find the best VAR model which has lowest AIC, AICc or BIC value. It is implemented using OLS per equation.
VAR(formula, ic = c("aicc", "aic", "bic"), ...)
formula |
Model specification (see "Specials" section). |
ic |
The information criterion used in selecting the model. |
... |
Further arguments for arima |
Exogenous regressors and common_xregs
can be specified in the model
formula.
A model specification.
The AR
special is used to specify the lag order for the auto-regression.
AR(p = 0:5)
p | The order of the auto-regressive (AR) terms. If multiple values are provided, the one which minimises ic will be chosen. |
Exogenous regressors can be included in an VAR model without explicitly using the xreg()
special. Common exogenous regressor specials as specified in common_xregs
can also be used. These regressors are handled using stats::model.frame()
, and so interactions and other functionality behaves similarly to stats::lm()
.
The inclusion of a constant in the model follows the similar rules to stats::lm()
, where including 1
will add a constant and 0
or -1
will remove the constant. If left out, the inclusion of a constant will be determined by minimising ic
.
xreg(...)
... | Bare expressions for the exogenous regressors (such as log(x) )
|
Forecasting: Principles and Practices, Vector autoregressions (section 11.2)
lung_deaths <- cbind(mdeaths, fdeaths) %>%
as_tsibble(pivot_longer = FALSE)
fit <- lung_deaths %>%
model(VAR(vars(mdeaths, fdeaths) ~ AR(3)))
report(fit)
fit %>%
forecast() %>%
autoplot(lung_deaths)
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