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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docstr: Updated data; new entries that did not exist in the previous dataset
  
  Returns
  -------
  data_updates : pd.DataFrame
      Index is as MultiIndex consisting of `update date` and
      `updated variable`. The columns are:
  
      - `forecast (prev)`: the previous forecast of the new entry,
        based on the information available in the previous dataset
        (recall that for these updated data points, the previous dataset
        had no observed value for them at all)
      - `observed`: the value of the new entry, as it is observed in the
        new dataset
  
  See also
  --------
  data_revisions
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code: def data_updates(self):
      """
      Updated data; new entries that did not exist in the previous dataset
  
      Returns
      -------
      data_updates : pd.DataFrame
          Index is as MultiIndex consisting of `update date` and
          `updated variable`. The columns are:
  
          - `forecast (prev)`: the previous forecast of the new entry,
            based on the information available in the previous dataset
            (recall that for these updated data points, the previous dataset
            had no observed value for them at all)
          - `observed`: the value of the new entry, as it is observed in the
            new dataset
  
      See also
      --------
      data_revisions
      """
      data = pd.concat(
          [
              self.update_realized.rename("observed"),
              self.update_forecasts.rename("forecast (prev)"),
          ],
          axis=1,
      ).sort_index()
      return data
  
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 7 for calling the NewsResults obj's underlying member method code with example usage: obj.data_updates() and returns: data