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: Minimal implementation of LS estimator for internal use
docstr_tok: ["Minimal","implementation","of","LS","estimator","for","internal","use"]
code: def _quick_ols(self, endog, exog):
      """
      Minimal implementation of LS estimator for internal use
      """
      xpxi = np.linalg.inv(exog.T.dot(exog))
      xpy = exog.T.dot(endog)
      nobs, k_exog = exog.shape
      b = xpxi.dot(xpy)
      e = endog - exog.dot(b)
      sigma2 = e.T.dot(e) / (nobs - k_exog)
      return b / np.sqrt(np.diag(sigma2 * xpxi))
  
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 3 for calling the ZivotAndrewsUnitRoot obj's underlying member method code with example usage: obj._quick_ols(endog, exog) and returns: unknown