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: Critical values for the three different models specified for the
  Zivot-Andrews unit-root test.
  
  Notes
  -----
  The p-values are generated through Monte Carlo simulation using
  100,000 replications and 2000 data points.
docstr_tok: ["Critical","values","for","the","three","different","models","specified","for","the","Zivot-Andrews","unit-root","test",".","Notes","--","--","-","The","p-values","are","generated","through","Monte","Carlo","simulation","using","100,000","replications","and","2000","data","points","."]
code: def __init__(self):
      """
      Critical values for the three different models specified for the
      Zivot-Andrews unit-root test.
  
      Notes
      -----
      The p-values are generated through Monte Carlo simulation using
      100,000 replications and 2000 data points.
      """
      self._za_critical_values = {}
      # constant-only model
      self._c = (
          (0.001, -6.78442),
          (0.100, -5.83192),
          (0.200, -5.68139),
          (0.300, -5.58461),
          (0.400, -5.51308),
          (0.500, -5.45043),
          (0.600, -5.39924),
          (0.700, -5.36023),
          (0.800, -5.33219),
          (0.900, -5.30294),
          (1.000, -5.27644),
          (2.500, -5.03340),
          (5.000, -4.81067),
          (7.500, -4.67636),
          (10.000, -4.56618),
          (12.500, -4.48130),
          (15.000, -4.40507),
          (17.500, -4.33947),
          (20.000, -4.28155),
          (22.500, -4.22683),
          (25.000, -4.17830),
          (27.500, -4.13101),
          (30.000, -4.08586),
          (32.500, -4.04455),
          (35.000, -4.00380),
          (37.500, -3.96144),
          (40.000, -3.92078),
          (42.500, -3.88178),
          (45.000, -3.84503),
          (47.500, -3.80549),
          (50.000, -3.77031),
          (52.500, -3.73209),
          (55.000, -3.69600),
          (57.500, -3.65985),
          (60.000, -3.62126),
          (65.000, -3.54580),
          (70.000, -3.46848),
          (75.000, -3.38533),
          (80.000, -3.29112),
          (85.000, -3.17832),
          (90.000, -3.04165),
          (92.500, -2.95146),
          (95.000, -2.83179),
          (96.000, -2.76465),
          (97.000, -2.68624),
          (98.000, -2.57884),
          (99.000, -2.40044),
          (99.900, -1.88932),
      )
      self._za_critical_values["c"] = np.asarray(self._c)
      # trend-only model
      self._t = (
          (0.001, -83.9094),
          (0.100, -13.8837),
          (0.200, -9.13205),
          (0.300, -6.32564),
          (0.400, -5.60803),
          (0.500, -5.38794),
          (0.600, -5.26585),
          (0.700, -5.18734),
          (0.800, -5.12756),
          (0.900, -5.07984),
          (1.000, -5.03421),
          (2.500, -4.65634),
          (5.000, -4.40580),
          (7.500, -4.25214),
          (10.000, -4.13678),
          (12.500, -4.03765),
          (15.000, -3.95185),
          (17.500, -3.87945),
          (20.000, -3.81295),
          (22.500, -3.75273),
          (25.000, -3.69836),
          (27.500, -3.64785),
          (30.000, -3.59819),
          (32.500, -3.55146),
          (35.000, -3.50522),
          (37.500, -3.45987),
          (40.000, -3.41672),
          (42.500, -3.37465),
          (45.000, -3.33394),
          (47.500, -3.29393),
          (50.000, -3.25316),
          (52.500, -3.21244),
          (55.000, -3.17124),
          (57.500, -3.13211),
          (60.000, -3.09204),
          (65.000, -3.01135),
          (70.000, -2.92897),
          (75.000, -2.83614),
          (80.000, -2.73893),
          (85.000, -2.62840),
          (90.000, -2.49611),
          (92.500, -2.41337),
          (95.000, -2.30820),
          (96.000, -2.25797),
          (97.000, -2.19648),
          (98.000, -2.11320),
          (99.000, -1.99138),
          (99.900, -1.67466),
      )
      self._za_critical_values["t"] = np.asarray(self._t)
      # constant + trend model
      self._ct = (
          (0.001, -38.17800),
          (0.100, -6.43107),
          (0.200, -6.07279),
          (0.300, -5.95496),
          (0.400, -5.86254),
          (0.500, -5.77081),
          (0.600, -5.72541),
          (0.700, -5.68406),
          (0.800, -5.65163),
          (0.900, -5.60419),
          (1.000, -5.57556),
          (2.500, -5.29704),
          (5.000, -5.07332),
          (7.500, -4.93003),
          (10.000, -4.82668),
          (12.500, -4.73711),
          (15.000, -4.66020),
          (17.500, -4.58970),
          (20.000, -4.52855),
          (22.500, -4.47100),
          (25.000, -4.42011),
          (27.500, -4.37387),
          (30.000, -4.32705),
          (32.500, -4.28126),
          (35.000, -4.23793),
          (37.500, -4.19822),
          (40.000, -4.15800),
          (42.500, -4.11946),
          (45.000, -4.08064),
          (47.500, -4.04286),
          (50.000, -4.00489),
          (52.500, -3.96837),
          (55.000, -3.93200),
          (57.500, -3.89496),
          (60.000, -3.85577),
          (65.000, -3.77795),
          (70.000, -3.69794),
          (75.000, -3.61852),
          (80.000, -3.52485),
          (85.000, -3.41665),
          (90.000, -3.28527),
          (92.500, -3.19724),
          (95.000, -3.08769),
          (96.000, -3.03088),
          (97.000, -2.96091),
          (98.000, -2.85581),
          (99.000, -2.71015),
          (99.900, -2.28767),
      )
      self._za_critical_values["ct"] = np.asarray(self._ct)
  
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 1 to create a new ZivotAndrewsUnitRoot object  with example: obj = ZivotAndrewsUnitRoot()