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
code: def conf_int(self, alpha=0.05, cols=None):
      ci = self._conf_int(alpha, cols)
      if not self._use_pandas:
          return ci
      ci_names = ("lower", "upper")
      row_names = self.model.data.row_labels
      col_names = self.model.data.param_names
      if cols is not None:
          col_names = [col_names[i] for i in cols]
      mi = MultiIndex.from_product((col_names, ci_names))
      ci = np.reshape(np.swapaxes(ci, 1, 2), (ci.shape[0], -1))
      return DataFrame(ci, columns=mi, index=row_names)
  
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 30 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj.conf_int(alpha, cols) and returns: DataFrame, ci