class_docstr: A VAR model is a generalisation of the univariate autoregressive.
  
  Direct interface for `statsmodels.tsa.vector_ar`
  A model for forecasting a vector of time series[1].
  
  Parameters
  ----------
  maxlags: int or None (default=None)
      Maximum number of lags to check for order selection,
      defaults to 12 * (nobs/100.)**(1./4)
  method : str (default="ols")
      Estimation method to use
  verbose : bool (default=False)
      Print order selection output to the screen
  trend : str {"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_state : int, 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.
  
  References
  ----------
  [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.
  
  Examples
  --------
  >>> from sktime.forecasting.var import VAR
  >>> from sktime.datasets import load_longley
  >>> _, y = load_longley()
  >>> forecaster = VAR()  # doctest: +SKIP
  >>> forecaster.fit(y)  # doctest: +SKIP
  VAR(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])  # doctest: +SKIP
code: def __init__(
      self,
      maxlags=None,
      method="ols",
      verbose=False,
      trend="c",
      missing="none",
      dates=None,
      freq=None,
      ic=None,
      random_state=None,
  ):
      # Model params
      self.trend = trend
      self.maxlags = maxlags
      self.method = method
      self.verbose = verbose
      self.missing = missing
      self.dates = dates
      self.freq = freq
      self.ic = ic
  
      super().__init__(random_state=random_state)
  
file_path: sktime/sktime/forecasting/var.py
imports: import itertools
  from collections import OrderedDict
  import numpy
  import pandas
  from sktime.forecasting.base.adapters import _StatsModelsAdapter
label_desc: Use image node_id 1 to create a new VAR object from inherited base classes: _StatsModelsAdapter with example: obj = VAR(maxlags, method, verbose, trend, missing, dates, freq, ic, random_state)