class_docstr: Test for unit root order 1 via the Phillips-Perron Unit Root Test.
  
  Direct interface to ``PhillipsPerron`` test from the ``arch`` package.
  
  Uses ``arch.unitroot.PhillipsPerron`` 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, optional
      The number of lags to use in the Newey-West estimator of the long-run
      covariance. If omitted or None, the lag length is set automatically to
      ``12 * (nobs/100) ** (1/4)``
  trend : {"n", "c", "ct"}, optional
      The trend component to include in the test
  
      - "n" - No trend components
      - "c" - Include a constant (Default)
      - "ct" - Include a constant and linear time trend
  
  test_type : {"tau", "rho"}
      The test to use when computing the test statistic. "tau" is based on
      the t-stat and "rho" uses a test based on nobs times the re-centered
      regression coefficient
  
  Attributes
  ----------
  stationary_ : bool
      whether the series in ``fit`` is integrated of order 1
      more precisely, whether the null of the Phillips-Perron test is rejected at
      ``p_threshold``
  test_statistic_ : float
      The PP test statistic, of running ``PhillipsPerron`` on ``y`` in ``fit``
  pvalue_ : float : float
      p-value obtained when running ``PhillipsPerron`` 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 StationarityPhillipsPerron
  >>>
  >>> X = load_airline()  # doctest: +SKIP
  >>> sty_est = StationarityPhillipsPerron()  # doctest: +SKIP
  >>> sty_est.fit(X)  # doctest: +SKIP
  StationarityPhillipsPerron(...)
  >>> sty_est.get_fitted_params()["stationary"]  # doctest: +SKIP
  False
docstr: Fit estimator and estimate parameters.
  
  private _fit containing the core logic, called from fit
  
  Writes to self:
      Sets fitted model attributes ending in "_".
  
  Parameters
  ----------
  X : {ndarray, Series}
  The data to test for a unit root
  
  Returns
  -------
  self : reference to self
docstr_tok: ["Fit","estimator","and","estimate","parameters",".","private","_fit","containing","the","core","logic",",","called","from","fit","Writes","to","self",":","Sets","fitted","model","attributes","ending","in","``","_","''",".","Parameters","--","--","--","--","--","X",":","{","ndarray",",","Series","}","The","data","to","test","for","a","unit","root","Returns","--","--","--","-","self",":","reference","to","self"]
code: def _fit(self, X):
      """Fit estimator and estimate parameters.
  
      private _fit containing the core logic, called from fit
  
      Writes to self:
          Sets fitted model attributes ending in "_".
  
      Parameters
      ----------
      X : {ndarray, Series}
      The data to test for a unit root
  
      Returns
      -------
      self : reference to self
      """
      from arch.unitroot import PhillipsPerron
  
      p_threshold = self.p_threshold
  
      result = PhillipsPerron(
          y=X,
          lags=self.lags,
          trend=self.trend,
          test_type=self.test_type,
      )
      self.test_statistic_ = result.stat
      self.pvalue = result.pvalue
      self.stationary_ = result.pvalue <= p_threshold
      self.used_lag_ = result._lags
  
      return self
  
imports: from sktime.param_est.base import BaseParamFitter
label_desc: Use image node_id 2 for calling the StationarityPhillipsPerron obj's underlying member method code with example usage: obj._fit(X) and returns: self