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 fvalue(self):
      if self._cov_type == "nonrobust":
          return self.mse_model / self.mse_resid
      else:
          nobs = self._params.shape[0]
          stat = np.full(nobs, np.nan)
          k = self._params.shape[1]
          r = np.eye(k)
          locs = list(range(k))
          if self.k_constant:
              locs.pop(self.model.const_idx)
          if not locs:
              return stat
          r = r[locs]
          vcv = self._cov_params
          rvcvr = r @ vcv @ r.T
          p = self._params
          for i in range(nobs):
              rp = p[i : i + 1] @ r.T
              denom = rp.shape[1]
              inv_cov = np.linalg.inv(rvcvr[i])
              stat[i] = np.squeeze(rp @ inv_cov @ rp.T) / denom
          return stat
  
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 25 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj.fvalue() and returns: unknown, stat, stat