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: Wrap output as pandas Series or DataFrames as needed
docstr_tok: ["Wrap","output","as","pandas","Series","or","DataFrames","as","needed"]
code: def _wrap(self, val):
      """Wrap output as pandas Series or DataFrames as needed"""
      if not self._use_pandas:
          return val
      col_names = self.model.data.param_names
      row_names = self.model.data.row_labels
      if val.ndim == 1:
          return Series(val, index=row_names)
      if val.ndim == 2:
          return DataFrame(val, columns=col_names, index=row_names)
      else:  # ndim == 3
          mi = MultiIndex.from_product((row_names, col_names))
          val = np.reshape(val, (-1, val.shape[-1]))
          return DataFrame(val, columns=col_names, index=mi)
  
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 2 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj._wrap(val) and returns: val, Series, DataFrame, DataFrame