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 __init__(
      self, model, store: RollingStore, k_constant, use_t, cov_type
  ):
      self.model = model
      self._params = store.params
      self._ssr = store.ssr
      self._llf = store.llf
      self._nobs = store.nobs
      self._s2 = store.s2
      self._xpxi = store.xpxi
      self._xepxe = store.xeex
      self._centered_tss = store.centered_tss
      self._uncentered_tss = store.uncentered_tss
      self._k_constant = k_constant
      self._nvar = self._xpxi.shape[-1]
      if use_t is None:
          use_t = cov_type == "nonrobust"
      self._use_t = use_t
      self._cov_type = cov_type
      self._use_pandas = self.model.data.row_labels is not None
      self._data_attr = []
      self._cache = {}
  
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 1 to create a new RollingRegressionResults object  with example: obj = RollingRegressionResults(model, store, k_constant, use_t, cov_type)