class_docstr: Impacts of data revisions and news on estimates of variables of interest
  
  Parameters
  ----------
  news_results : SimpleNamespace instance
      Results from `KalmanSmoother.news`.
  model : MLEResults
      The results object associated with the model from which the NewsResults
      was generated.
  updated : MLEResults
      The results object associated with the model containing the updated
      dataset.
  previous : MLEResults
      The results object associated with the model containing the previous
      dataset.
  impacted_variable : str, list, array, or slice, optional
      Observation variable label or slice of labels specifying particular
      impacted variables to display in output. The impacted variable(s)
      describe the variables that were *affected* by the news. If you do not
      know the labels for the variables, check the `endog_names` attribute of
      the model instance.
  tolerance : float, optional
      The numerical threshold for determining zero impact. Default is that
      any impact less than 1e-10 is assumed to be zero.
  row_labels : iterable
      Row labels (often dates) for the impacts of the revisions and news.
  
  Attributes
  ----------
  total_impacts : pd.DataFrame
      Updates to forecasts of impacted variables from both news and data
      revisions, E[y^i | post] - E[y^i | previous].
  update_impacts : pd.DataFrame
      Updates to forecasts of impacted variables from the news,
      E[y^i | post] - E[y^i | revisions] where y^i are the impacted variables
      of interest.
  revision_impacts : pd.DataFrame
      Updates to forecasts of impacted variables from all data revisions,
      E[y^i | revisions] - E[y^i | previous].
  news : pd.DataFrame
      The unexpected component of the updated data,
      E[y^u | post] - E[y^u | revisions] where y^u are the updated variables.
  weights : pd.DataFrame
      Weights describing the effect of news on variables of interest.
  revisions : pd.DataFrame
      The revisions between the current and previously observed data, for
      revisions for which detailed impacts were computed.
  revisions_all : pd.DataFrame
      The revisions between the current and previously observed data,
      y^r_{revised} - y^r_{previous} where y^r are the revised variables.
  revision_weights : pd.DataFrame
      Weights describing the effect of revisions on variables of interest,
      for revisions for which detailed impacts were computed.
  revision_weights_all : pd.DataFrame
      Weights describing the effect of revisions on variables of interest,
      with a new entry that includes NaNs for the revisions for which
      detailed impacts were not computed.
  update_forecasts : pd.DataFrame
      Forecasts based on the previous dataset of the variables that were
      updated, E[y^u | previous].
  update_realized : pd.DataFrame
      Actual observed data associated with the variables that were
      updated, y^u
  revisions_details_start : int
      Integer index of first period in which detailed revision impacts were
      computed.
  revision_detailed_impacts : pd.DataFrame
      Updates to forecasts of impacted variables from data revisions with
      detailed impacts, E[y^i | revisions] - E[y^i | grouped revisions].
  revision_grouped_impacts : pd.DataFrame
      Updates to forecasts of impacted variables from data revisions that
      were grouped together, E[y^i | grouped revisions] - E[y^i | previous].
  revised_prev : pd.DataFrame
      Previously observed data associated with the variables that were
      revised, for revisions for which detailed impacts were computed.
  revised_prev_all : pd.DataFrame
      Previously observed data associated with the variables that were
      revised, y^r_{previous}
  revised : pd.DataFrame
      Currently observed data associated with the variables that were
      revised, for revisions for which detailed impacts were computed.
  revised_all : pd.DataFrame
      Currently observed data associated with the variables that were
      revised, y^r_{revised}
  prev_impacted_forecasts : pd.DataFrame
      Previous forecast of the variables of interest, E[y^i | previous].
  post_impacted_forecasts : pd.DataFrame
      Forecast of the variables of interest after taking into account both
      revisions and updates, E[y^i | post].
  revisions_iloc : pd.DataFrame
      The integer locations of the data revisions in the dataset.
  revisions_ix : pd.DataFrame
      The label-based locations of the data revisions in the dataset.
  revisions_iloc_detailed : pd.DataFrame
      The integer locations of the data revisions in the dataset for which
      detailed impacts were computed.
  revisions_ix_detailed : pd.DataFrame
      The label-based locations of the data revisions in the dataset for
      which detailed impacts were computed.
  updates_iloc : pd.DataFrame
      The integer locations of the updated data points.
  updates_ix : pd.DataFrame
      The label-based locations of updated data points.
  state_index : array_like
      Index of state variables used to compute impacts.
  
  References
  ----------
  .. [1] Bańbura, Marta, and Michele Modugno.
         "Maximum likelihood estimation of factor models on datasets with
         arbitrary pattern of missing data."
         Journal of Applied Econometrics 29, no. 1 (2014): 133-160.
  .. [2] Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin.
         "Nowcasting."
         The Oxford Handbook of Economic Forecasting. July 8, 2011.
  .. [3] Bańbura, Marta, Domenico Giannone, Michele Modugno, and Lucrezia
         Reichlin.
         "Now-casting and the real-time data flow."
         In Handbook of economic forecasting, vol. 2, pp. 195-237.
         Elsevier, 2013.
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code: def get_details(self, include_revisions=True, include_updates=True):
      details = []
      if include_updates:
          details.append(
              self.details_by_impact.rename(
                  columns={"forecast (prev)": "previous"}
              )
          )
      if include_revisions:
          tmp = self.revision_details_by_impact.rename_axis(
              index={
                  "revision date": "update date",
                  "revised variable": "updated variable",
              }
          )
          tmp = tmp.rename(
              columns={
                  "revised": "observed",
                  "observed (prev)": "previous",
                  "revision": "news",
              }
          )
          details.append(tmp)
      if not (include_updates or include_revisions):
          details.append(
              self.details_by_impact.rename(
                  columns={"forecast (prev)": "previous"}
              ).iloc[:0]
          )
  
      return pd.concat(details)
  
imports: from statsmodels.compat.pandas import FUTURE_STACK
  import numpy
  import pandas
  from statsmodels.iolib.summary import Summary
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.iolib.tableformatting import fmt_params
label_desc: Use image node_id 19 for calling the NewsResults obj's underlying member method code with example usage: obj.get_details(include_revisions, include_updates) and returns: pd