class_docstr: Test for stationarity via the variance ratio test for random walks.
  
  Direct interface to ``VarianceRatio`` test from the `arch` package.
  
  Uses ``arch.unitroot.VarianceRatio`` as a test for unit roots,
  and derives a boolean statement whether a series is stationary.
  
  Also returns test results for the unit root test as fitted parameters.
  
  Parameters
  ----------
  lags : int
      The number of periods to used in the multi-period variance, which is
      the numerator of the test statistic.  Must be at least 2
  trend : {"n", "c"}, optional
      "c" allows for a non-zero drift in the random walk, while "n" requires
      that the increments to y are mean 0
  overlap : bool, optional
      Indicates whether to use all overlapping blocks.  Default is True.  If
      False, the number of observations in y minus 1 must be an exact
      multiple of lags.  If this condition is not satisfied, some values at
      the end of y will be discarded.
  robust : bool, optional
      Indicates whether to use heteroskedasticity robust inference. Default
      is True.
  debiased : bool, optional
      Indicates whether to use a debiased version of the test. Default is
      True. Only applicable if overlap is True.
  
  Attributes
  ----------
  stationary_ : bool
      whether the series in ``fit`` is stationary according to the test
      more precisely, whether the null of the variance ratio test is accepted at
      ``p_threshold``
  test_statistic_ : float
      The VR test statistic, of running ``VarianceRatio`` on ``y`` in ``fit``
  pvalue_ : float : float
      p-value obtained when running ``VarianceRatio`` on ``y`` in ``fit``
  usedlag_ : int
      The number of lags used in the test.
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.param_est.stationarity import StationarityVarianceRatio
  >>>
  >>> X = load_airline()  # doctest: +SKIP
  >>> sty_est = StationarityVarianceRatio()  # doctest: +SKIP
  >>> sty_est.fit(X)  # doctest: +SKIP
  StationarityVarianceRatio(...)
  >>> sty_est.get_fitted_params()["stationary"]  # doctest: +SKIP
  True
code: def __init__(
      self,
      lags=2,
      trend="c",
      overlap=True,
      robust=True,
      debiased=True,
      p_threshold=0.05,
  ):
      self.lags = lags
      self.trend = trend
      self.overlap = overlap
      self.robust = robust
      self.debiased = debiased
      self.p_threshold = p_threshold
      super().__init__()
  
imports: from sktime.param_est.base import BaseParamFitter
label_desc: Use image node_id 1 to create a new StationarityVarianceRatio object from inherited base classes: BaseParamFitter with example: obj = StationarityVarianceRatio(lags, trend, overlap, robust, debiased, p_threshold)