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: Plot the recursively estimated coefficients on a given variable
  
  Parameters
  ----------
  variables : {int, str, Iterable[int], Iterable[str], None}, optional
      Integer index or string name of the variables whose coefficients
      to plot. Can also be an iterable of integers or strings. Default
      plots all coefficients.
  alpha : float, optional
      The confidence intervals for the coefficient are (1 - alpha)%. Set
      to None to exclude confidence intervals.
  legend_loc : str, optional
      The location of the legend in the plot. Default is upper left.
  fig : Figure, optional
      If given, subplots are created in this figure instead of in a new
      figure. Note that the grid will be created in the provided
      figure using `fig.add_subplot()`.
  figsize : tuple, optional
      If a figure is created, this argument allows specifying a size.
      The tuple is (width, height).
  
  Returns
  -------
  Figure
      The matplotlib Figure object.
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code: def plot_recursive_coefficient(
      self,
      variables=None,
      alpha=0.05,
      legend_loc="upper left",
      fig=None,
      figsize=None,
  ):
      r"""
      Plot the recursively estimated coefficients on a given variable
  
      Parameters
      ----------
      variables : {int, str, Iterable[int], Iterable[str], None}, optional
          Integer index or string name of the variables whose coefficients
          to plot. Can also be an iterable of integers or strings. Default
          plots all coefficients.
      alpha : float, optional
          The confidence intervals for the coefficient are (1 - alpha)%. Set
          to None to exclude confidence intervals.
      legend_loc : str, optional
          The location of the legend in the plot. Default is upper left.
      fig : Figure, optional
          If given, subplots are created in this figure instead of in a new
          figure. Note that the grid will be created in the provided
          figure using `fig.add_subplot()`.
      figsize : tuple, optional
          If a figure is created, this argument allows specifying a size.
          The tuple is (width, height).
  
      Returns
      -------
      Figure
          The matplotlib Figure object.
      """
      from statsmodels.graphics.utils import _import_mpl, create_mpl_fig
  
      if alpha is not None:
          ci = self._conf_int(alpha, None)
  
      row_labels = self.model.data.row_labels
      if row_labels is None:
          row_labels = np.arange(self._params.shape[0])
      k_variables = self._params.shape[1]
      param_names = self.model.data.param_names
      if variables is None:
          variable_idx = list(range(k_variables))
      else:
          if isinstance(variables, (int, str)):
              variables = [variables]
          variable_idx = []
          for i in range(len(variables)):
              variable = variables[i]
              if variable in param_names:
                  variable_idx.append(param_names.index(variable))
              elif isinstance(variable, int):
                  variable_idx.append(variable)
              else:
                  msg = (
                      "variable {} is not an integer and was not found "
                      "in the list of variable "
                      "names: {}".format(
                          variables[i], ", ".join(param_names)
                      )
                  )
                  raise ValueError(msg)
  
      _import_mpl()
      fig = create_mpl_fig(fig, figsize)
  
      loc = 0
      import pandas as pd
  
      if isinstance(row_labels, pd.PeriodIndex):
          row_labels = row_labels.to_timestamp()
      row_labels = np.asarray(row_labels)
      for i in variable_idx:
          ax = fig.add_subplot(len(variable_idx), 1, loc + 1)
          params = self._params[:, i]
          valid = ~np.isnan(self._params[:, i])
          row_lbl = row_labels[valid]
          ax.plot(row_lbl, params[valid])
          if alpha is not None:
              this_ci = np.reshape(ci[:, :, i], (-1, 2))
              if not np.all(np.isnan(this_ci)):
                  ax.plot(
                      row_lbl,
                      this_ci[:, 0][valid],
                      "k:",
                      label="Lower CI",
                  )
                  ax.plot(
                      row_lbl,
                      this_ci[:, 1][valid],
                      "k:",
                      label="Upper CI",
                  )
                  if loc == 0:
                      ax.legend(loc=legend_loc)
          ax.set_xlim(row_lbl[0], row_lbl[-1])
          ax.set_title(param_names[i])
          loc += 1
  
      fig.tight_layout()
      return fig
  
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 34 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj.plot_recursive_coefficient(variables, alpha, legend_loc, fig, figsize) and returns: fig