class_docstr: Class wrapper for Zivot-Andrews structural-break unit-root test
class_docstr_tok: ["Class","wrapper","for","Zivot-Andrews","structural-break","unit-root","test"]
docstr: Linear interpolation for Zivot-Andrews p-values and critical values
  
  Parameters
  ----------
  stat : float
      The ZA test statistic
  model : {"c","t","ct"}
      The model used when computing the ZA statistic. "c" is default.
  
  Returns
  -------
  pvalue : float
      The interpolated p-value
  cvdict : dict
      Critical values for the test statistic at the 1%, 5%, and 10%
      levels
  
  Notes
  -----
  The p-values are linear interpolated from the quantiles of the
  simulated ZA test statistic distribution
docstr_tok: ["Linear","interpolation","for","Zivot-Andrews","p-values","and","critical","values","Parameters","--","--","--","--","--","stat",":","float","The","ZA","test","statistic","model",":","{","``","c","''",",","''","t","''",",","''","ct","''","}","The","model","used","when","computing","the","ZA","statistic",".","``","c","''","is","default",".","Returns","--","--","--","-","pvalue",":","float","The","interpolated","p-value","cvdict",":","dict","Critical","values","for","the","test","statistic","at","the","1","%",",","5","%",",","and","10","%","levels","Notes","--","--","-","The","p-values","are","linear","interpolated","from","the","quantiles","of","the","simulated","ZA","test","statistic","distribution"]
code: def _za_crit(self, stat, model="c"):
      """
      Linear interpolation for Zivot-Andrews p-values and critical values
  
      Parameters
      ----------
      stat : float
          The ZA test statistic
      model : {"c","t","ct"}
          The model used when computing the ZA statistic. "c" is default.
  
      Returns
      -------
      pvalue : float
          The interpolated p-value
      cvdict : dict
          Critical values for the test statistic at the 1%, 5%, and 10%
          levels
  
      Notes
      -----
      The p-values are linear interpolated from the quantiles of the
      simulated ZA test statistic distribution
      """
      table = self._za_critical_values[model]
      pcnts = table[:, 0]
      stats = table[:, 1]
      # ZA cv table contains quantiles multiplied by 100
      pvalue = np.interp(stat, stats, pcnts) / 100.0
      cv = [1.0, 5.0, 10.0]
      crit_value = np.interp(cv, pcnts, stats)
      cvdict = {
          "1%": crit_value[0],
          "5%": crit_value[1],
          "10%": crit_value[2],
      }
      return pvalue, cvdict
  
imports: from __future__ import annotations
  from statsmodels.compat.numpy import lstsq
  from statsmodels.compat.pandas import deprecate_kwarg
  from statsmodels.compat.python import Literal, lzip
  from statsmodels.compat.scipy import _next_regular
  from typing import Union
  import warnings
  import numpy
  from numpy.linalg import LinAlgError
  import pandas
  from scipy import stats
  from scipy.interpolate import interp1d
  from scipy.signal import correlate
  from statsmodels.regression.linear_model import OLS, yule_walker
  from statsmodels.tools.sm_exceptions import CollinearityWarning, InfeasibleTestError, InterpolationWarning, MissingDataError, ValueWarning
  from statsmodels.tools.tools import Bunch, add_constant
  from statsmodels.tools.validation import array_like, bool_like, dict_like, float_like, int_like, string_like
  from statsmodels.tsa._bds import bds
  from statsmodels.tsa._innovations import innovations_algo, innovations_filter
  from statsmodels.tsa.adfvalues import mackinnoncrit, mackinnonp
  from statsmodels.tsa.tsatools import add_trend, lagmat, lagmat2ds
label_desc: Use image node_id 2 for calling the ZivotAndrewsUnitRoot obj's underlying member method code with example usage: obj._za_crit(stat, model) and returns: pvalue,  cvdict