VARIMA | R Documentation |
Estimates a VARIMA model of a given order.
VARIMA(formula, identification = c("kronecker_indices", "none"), ...)
## S3 method for class 'VARIMA'
forecast(
object,
new_data = NULL,
specials = NULL,
bootstrap = FALSE,
times = 5000,
...
)
## S3 method for class 'VARIMA'
fitted(object, ...)
## S3 method for class 'VARIMA'
residuals(object, ...)
## S3 method for class 'VARIMA'
tidy(x, ...)
## S3 method for class 'VARIMA'
glance(x, ...)
## S3 method for class 'VARIMA'
report(object, ...)
## S3 method for class 'VARIMA'
generate(x, new_data, specials, ...)
## S3 method for class 'VARIMA'
IRF(x, new_data, specials, impulse = NULL, orthogonal = FALSE, ...)
formula |
Model specification (see "Specials" section). |
identification |
The identification technique used to estimate the model. |
... |
Further arguments for arima |
object |
A model for which forecasts are required. |
new_data |
A tsibble containing the time points and exogenous regressors to produce forecasts for. |
specials |
(passed by |
bootstrap |
If |
times |
The number of sample paths to use in estimating the forecast distribution when |
x |
A fitted model. |
impulse |
A character string specifying the name of the variable that is shocked (the impulse variable). |
orthogonal |
If TRUE, orthogonalised impulse responses will be computed. |
Exogenous regressors and common_xregs
can be specified in the model
formula.
A model specification.
A one row tibble summarising the model's fit.
The pdq
special is used to specify non-seasonal components of the model.
pdq(p = 0:5, d = 0:2, q = 0:5)
p | The order of the non-seasonal auto-regressive (AR) terms. If multiple values are provided, the one which minimises ic will be chosen. |
d | The order of integration for non-seasonal differencing. If multiple values are provided, one of the values will be selected via repeated KPSS tests. |
q | The order of the non-seasonal moving average (MA) terms. If multiple values are provided, the one which minimises ic will be chosen. |
Exogenous regressors can be included in an VARIMA 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) )
|
MTS::VARMA()
, MTS::Kronfit()
.
library(tsibbledata)
aus_production %>%
autoplot(vars(Beer, Cement))
fit <- aus_production %>%
model(VARIMA(vars(Beer, Cement) ~ pdq(4,1,1), identification = "none"))
fit
fit %>%
forecast(h = 50) %>%
autoplot(tail(aus_production, 100))
fitted(fit)
residuals(fit)
tidy(fit)
glance(fit)
report(fit)
generate(fit, h = 10)
IRF(fit, h = 10, impulse = "Beer")
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