class_docstr: Results from rolling regressions
  
  Parameters
  ----------
  model : RollingWLS
      Model instance
  store : RollingStore
      Container for raw moving window results
  k_constant : bool
      Flag indicating that the model contains a constant
  use_t : bool
      Flag indicating to use the Student's t distribution when computing
      p-values.
  cov_type : str
      Name of covariance estimator
docstr: Estimated parameter covariance
  
  Returns
  -------
  array_like
      The estimated model covariances. If the original input is a numpy
      array, the returned covariance is a 3-d array with shape
      (nobs, nvar, nvar). If the original inputs are pandas types, then
      the returned covariance is a DataFrame with a MultiIndex with
      key (observation, variable), so that the covariance for
      observation with index i is cov.loc[i].
docstr_tok: ["Estimated","parameter","covariance","Returns","--","--","--","-","array_like","The","estimated","model","covariances",".","If","the","original","input","is","a","numpy","array",",","the","returned","covariance","is","a","3-d","array","with","shape","(","nobs",",","nvar",",","nvar",")",".","If","the","original","inputs","are","pandas","types",",","then","the","returned","covariance","is","a","DataFrame","with","a","MultiIndex","with","key","(","observation",",","variable",")",",","so","that","the","covariance","for","observation","with","index","i","is","cov.loc","[","i","]","."]
code: def cov_params(self):
      """
      Estimated parameter covariance
  
      Returns
      -------
      array_like
          The estimated model covariances. If the original input is a numpy
          array, the returned covariance is a 3-d array with shape
          (nobs, nvar, nvar). If the original inputs are pandas types, then
          the returned covariance is a DataFrame with a MultiIndex with
          key (observation, variable), so that the covariance for
          observation with index i is cov.loc[i].
      """
      return self._wrap(self._cov_params)
  
imports: from statsmodels.compat.numpy import lstsq
  from statsmodels.compat.pandas import Appender, Substitution, cache_readonly, call_cached_func, get_cached_doc
  from collections import namedtuple
  import numpy
  from pandas import DataFrame, MultiIndex, Series
  from scipy import stats
  from statsmodels.base import model
  from statsmodels.base.model import LikelihoodModelResults, Model
  from statsmodels.regression.linear_model import RegressionModel, RegressionResults
  from statsmodels.tools.validation import array_like, int_like, string_like
label_desc: Use image node_id 23 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj.cov_params() and returns: self