VAR

VAR model from statsmodels.

Quickstart

from sktime.forecasting.var import VAR

estimator = VAR(maxlags=None, method='ols', verbose=False, trend='c', missing='none', dates=None, freq=None, ic=None, random_state=None)

Parameters(9)

maxlags: int or None (default=None)

Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4)

methodstr (default=”ols”)

Estimation method to use

verbosebool (default=False)

Print order selection output to the screen

trendstr {“c”, “ct”, “ctt”, “n”} (default=”c”)

• “c” - add constant

• “ct” - constant and trend

• “ctt” - constant, linear and quadratic trend

• “n” - co constant, no trend

Note that these are prepended to the columns of the dataset.

missing: str, optional (default=’none’)

A string specifying if data is missing

freq: str, tuple, datetime.timedelta, DateOffset or None, optional (default=None)

A frequency specification for either dates or the row labels from the endog / exog data.

dates: array_like, optional (default=None)

An array like object containing dates.

ic: One of {‘aic’, ‘fpe’, ‘hqic’, ‘bic’, None} (default=None)

Information criterion to use for VAR order selection.

• “aic”: Akaike

• “fpe”: Final prediction error

• “hqic”: Hannan-Quinn

• “bic”: Bayesian a.k.a. Schwarz

random_stateint, RandomState instance or None, optional,

default=None - If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Examples

>>> from sktime.forecasting.var import VAR
>>> from sktime.datasets import load_longley
>>> _, y = load_longley ()
>>> forecaster = VAR ()
>>> forecaster. fit (y) VAR(
... )
>>> y_pred = forecaster. predict (fh = [1, 2, 3 ])

References

1. [1] Athanasopoulos, G., Poskitt, D. S., & Vahid, F. (2012). Two canonical VARMA forms: Scalar component models vis-à-vis the echelon form. Econometric Reviews, 31(1), 60-83, 2012.