class_docstr: Results class for Engle-Granger cointegration tests.
  
  Parameters
  ----------
  stat : float
      The Engle-Granger test statistic.
  pvalue : float
      The pvalue of the Engle-Granger test statistic.
  crit_vals : Series
      The critical values of the Engle-Granger specific to the sample size
      and model dimension.
  null : str
      The null hypothesis.
  alternative : str
      The alternative hypothesis.
  trend : str
      The model's trend description.
  order : int
      The number of stochastic trends in the null distribution.
  adf : ADF
      The ADF instance used to perform the test and lag selection.
  xsection : RegressionResults
      The OLS results used in the cross-sectional regression.
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docstr: The estimated coefficient in the Dickey-Fuller Test
  
  Returns
  -------
  float
      The coefficient.
  
  Notes
  -----
  The value returned is :math:`\hat{\rho}=\hat{\gamma}+1` from the ADF
  regression
  
  .. math::
  
      \Delta y_t = \gamma y_{t-1} + \sum_{i=1}^p \delta_i \Delta y_{t-i}
                   + \epsilon_t
docstr_tok: ["The","estimated","coefficient","in","the","Dickey-Fuller","Test","Returns","--","--","--","-","float","The","coefficient",".","Notes","--","--","-","The","value","returned","is",":","math",":","`","\\hat","{","\\rho","}","=\\hat","{","\\gamma","}","+1","`","from","the","ADF","regression","..","math",":",":","\\Delta","y_t","=","\\gamma","y_","{","t-1","}","+","\\sum_","{","i=1","}","^p","\\delta_i","\\Delta","y_","{","t-i","}","+","\\epsilon_t"]
code: def rho(self) -> float:
      r"""
      The estimated coefficient in the Dickey-Fuller Test
  
      Returns
      -------
      float
          The coefficient.
  
      Notes
      -----
      The value returned is :math:`\hat{\rho}=\hat{\gamma}+1` from the ADF
      regression
  
      .. math::
  
          \Delta y_t = \gamma y_{t-1} + \sum_{i=1}^p \delta_i \Delta y_{t-i}
                       + \epsilon_t
      """
  
      param0, *_ = self._adf.regression.params
      return 1 + param0
  
imports: from __future__ import annotations
  import numpy
  import pandas
  from scipy import stats
  from statsmodels.iolib.summary import Summary
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.regression.linear_model import RegressionResults
  from arch.typing import ArrayLike1D, ArrayLike2D, Literal, UnitRootTrend
  from arch.unitroot._shared import ResidualCointegrationTestResult, _check_cointegrating_regression, _cross_section
  from arch.unitroot.critical_values.engle_granger import CV_PARAMETERS, LARGE_PARAMETERS, SMALL_PARAMETERS, TAU_MAX, TAU_MIN, TAU_STAR
  from arch.unitroot.unitroot import ADF, TREND_DESCRIPTION
label_desc: Use image node_id 4 for calling the EngleGrangerTestResults obj's underlying member method code with example usage: obj.rho() and returns: unknown