text: # -*- coding: utf-8 -*-
  """
  News for state space models
  
  Author: Chad Fulton
  License: BSD-3
  """
  
  import numpy as np
  import pandas as pd
  
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.iolib.summary import Summary
  from statsmodels.iolib.tableformatting import fmt_params
  
  
  class NewsResults(object):
      """
      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.Series
          Updates to forecasts of impacted variables from both news and data
          revisions, E[y^i | post] - E[y^i | previous].
      update_impacts : pd.Series
          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.Series
          Updates to forecasts of impacted variables from data revisions,
          E[y^i | revisions] - E[y^i | previous].
      news : pd.Series
          The unexpected component of the updated data,
          E[y^u | post] - E[y^u | revisions] where y^u are the updated variables.
      weights : pd.Series
          Weights describing the effect of news on variables of interest.
      revisions : pd.Series
          The revisions betwen the current and previously observed data
          y^r_{revised} - y^r_{previous} where y^r are the revised variables.
      revision_weights : pd.Series
          Weights describing the effect of revisions on variables of interest.
      update_forecasts : pd.Series
          Forecasts based on the previous dataset of the variables that were
          updated, E[y^u | previous].
      update_realized : pd.Series
          Actual observed data associated with the variables that were
          updated, y^u
      revised_prev : pd.Series
          Previously observed data associated with the variables that were
          revised, y^r_{previous}
      revised : pd.Series
          Currently observed data associated with the variables that were
          revised, y^r_{revised}
      prev_impacted_forecasts : pd.Series
          Previous forecast of the variables of interest, E[y^i | previous].
      post_impacted_forecasts : pd.Series
          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.
      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.
      """
      def __init__(self, news_results, model, updated, previous,
                   impacted_variable=None, tolerance=1e-10, row_labels=None):
          # Note: `model` will be the same as one of `revised` or `previous`, but
          # we need to save it as self.model so that the `predict_dates`, which
          # were generated by the `_get_prediction_index` call, will be available
          # for use by the base wrapping code.
          self.model = model
          self.updated = updated
          self.previous = previous
          self.news_results = news_results
          self._impacted_variable = impacted_variable
          self._tolerance = tolerance
          self.row_labels = row_labels
          self.params = []  # required for `summary` to work
  
          self.endog_names = self.updated.model.endog_names
          self.k_endog = len(self.endog_names)
  
          index = self.updated.model._index
          columns = np.atleast_1d(self.endog_names)
  
          # E[y^i | post]
          self.post_impacted_forecasts = pd.DataFrame(
              news_results.post_impacted_forecasts.T,
              index=self.row_labels, columns=columns)
          # E[y^i | previous]
          self.prev_impacted_forecasts = pd.DataFrame(
              news_results.prev_impacted_forecasts.T,
              index=self.row_labels, columns=columns)
          # E[y^i | post] - E[y^i | revisions]
          self.update_impacts = pd.DataFrame(
              news_results.update_impacts,
              index=self.row_labels, columns=columns)
          # E[y^i | revisions] - E[y^i | previous]
          self.revision_impacts = pd.DataFrame(
              news_results.revision_impacts,
              index=self.row_labels, columns=columns)
          # E[y^i | post] - E[y^i | previous]
          self.total_impacts = (self.post_impacted_forecasts -
                                self.prev_impacted_forecasts)
  
          # Indices of revisions and updates
          self.revisions_iloc = pd.DataFrame(
              list(zip(*news_results.revisions_ix)),
              index=['revision date', 'revised variable']).T
          iloc = self.revisions_iloc
          if len(iloc) > 0:
              self.revisions_ix = pd.DataFrame({
                  'revision date': index[iloc['revision date']],
                  'revised variable': columns[iloc['revised variable']]})
          else:
              self.revisions_ix = iloc.copy()
  
          self.updates_iloc = pd.DataFrame(
              list(zip(*news_results.updates_ix)),
              index=['update date', 'updated variable']).T
          iloc = self.updates_iloc
          if len(iloc) > 0:
              self.updates_ix = pd.DataFrame({
                  'update date': index[iloc['update date']],
                  'updated variable': columns[iloc['updated variable']]})
          else:
              self.updates_ix = iloc.copy()
  
          # Index of the state variables used
          self.state_index = news_results.state_index
  
          # Wrap forecasts and forecasts errors
          r_ix = pd.MultiIndex.from_arrays([
              self.revisions_ix['revision date'],
              self.revisions_ix['revised variable']])
          u_ix = pd.MultiIndex.from_arrays([
              self.updates_ix['update date'],
              self.updates_ix['updated variable']])
  
          # E[y^u | post] - E[y^u | revisions]
          if news_results.news is None:
              self.news = pd.Series([], index=u_ix, name='news',
                                    dtype=model.params.dtype)
          else:
              self.news = pd.Series(news_results.news, index=u_ix, name='news')
          # E[y^u | revisions] - E[y^u | previous]
          if news_results.revisions is None:
              self.revisions = pd.Series([], index=r_ix, name='revision',
                                         dtype=model.params.dtype)
          else:
              self.revisions = pd.Series(news_results.revisions, index=r_ix,
                                         name='revision')
          # E[y^u | revised]
          if news_results.update_forecasts is None:
              self.update_forecasts = pd.Series([], index=u_ix,
                                                dtype=model.params.dtype)
          else:
              self.update_forecasts = pd.Series(
                  news_results.update_forecasts, index=u_ix)
          # y^r_{revised}
          if news_results.revised is None:
              self.revised = pd.Series([], index=r_ix, dtype=model.params.dtype,
                                       name='revised')
          else:
              self.revised = pd.Series(news_results.revised, index=r_ix,
                                       name='revised')
          # y^r_{previous}
          if news_results.revised_prev is None:
              self.revised_prev = pd.Series([], index=r_ix,
                                            dtype=model.params.dtype)
          else:
              self.revised_prev = pd.Series(
                  news_results.revised_prev, index=r_ix)
          # y^u
          if news_results.update_realized is None:
              self.update_realized = pd.Series([], index=u_ix,
                                               dtype=model.params.dtype)
          else:
              self.update_realized = pd.Series(
                  news_results.update_realized, index=u_ix)
          cols = pd.MultiIndex.from_product([self.row_labels, columns])
          # reshaped version of gain matrix E[y A'] E[A A']^{-1}
          if len(self.updates_iloc):
              weights = news_results.gain.reshape(
                  len(cols), len(u_ix))
          else:
              weights = np.zeros((len(cols), len(u_ix)))
          self.weights = pd.DataFrame(weights, index=cols, columns=u_ix).T
          self.weights.columns.names = ['impact date', 'impacted variable']
  
          # reshaped version of revision_weights
          if len(self.revisions_iloc):
              revision_weights = news_results.revision_weights.reshape(
                  len(cols), len(r_ix))
          else:
              revision_weights = np.zeros((len(cols), len(r_ix)))
          self.revision_weights = pd.DataFrame(
              revision_weights, index=cols, columns=r_ix).T
          self.revision_weights.columns.names = [
              'impact date', 'impacted variable']
  
      @property
      def impacted_variable(self):
          return self._impacted_variable
  
      @impacted_variable.setter
      def impacted_variable(self, value):
          self._impacted_variable = value
  
      @property
      def tolerance(self):
          return self._tolerance
  
      @tolerance.setter
      def tolerance(self, value):
          self._tolerance = value
  
      @property
      def data_revisions(self):
          """
          Revisions to data points that existed in the previous dataset
  
          Returns
          -------
          data_revisions : pd.DataFrame
              Index is as MultiIndex consisting of `revision date` and
              `revised variable`. The columns are:
  
              - `observed (prev)`: the value of the data as it was observed
                in the previous dataset.
              - `revised`: the revised value of the data, as it is observed
                in the new dataset
  
          See also
          --------
          data_updates
          """
          # Save revisions data
          data = pd.concat([
              self.revised.rename('revised'),
              self.revised_prev.rename('observed (prev)')
          ], axis=1).sort_index()
          return data
  
      @property
      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
  
      @property
      def details_by_impact(self):
          """
          Details of forecast revisions from news, organized by impacts first
  
          Returns
          -------
          details : pd.DataFrame
              Index is as MultiIndex consisting of:
  
              - `impact date`: the date of the impact on the variable of interest
              - `impacted variable`: the variable that is being impacted
              - `update date`: the date of the data update, that results in
                `news` that impacts the forecast of variables of interest
              - `updated variable`: the variable being updated, that results in
                `news` that impacts the forecast of variables of interest
  
              The columns are:
  
              - `forecast (prev)`: the previous forecast of the new entry,
                based on the information available in the previous dataset
              - `observed`: the value of the new entry, as it is observed in the
                new dataset
              - `news`: the news associated with the update (this is just the
                forecast error: `observed` - `forecast (prev)`)
              - `weight`: the weight describing how the `news` effects the
                forecast of the variable of interest
              - `impact`: the impact of the `news` on the forecast of the
                variable of interest
  
          Notes
          -----
          This table decomposes updated forecasts of variables of interest from
          the `news` associated with each updated datapoint from the new data
          release.
  
          This table does not summarize the impacts or show the effect of
          revisions. That information can be found in the `impacts` or
          `revision_details_by_impact` tables.
  
          This form of the details table is organized so that the impacted
          dates / variables are first in the index. This is convenient for
          slicing by impacted variables / dates to view the details of data
          updates for a particular variable or date.
  
          However, since the `forecast (prev)` and `observed` columns have a lot
          of duplication, printing the entire table gives a result that is less
          easy to parse than that produced by the `details_by_update` property.
          `details_by_update` contains the same information but is organized to
          be more convenient for displaying the entire table of detailed updates.
          At the same time, `details_by_update` is less convenient for
          subsetting.
  
          See Also
          --------
          details_by_update
          revision_details_by_update
          impacts
          """
          df = self.weights.stack(level=[0, 1]).rename('weight').to_frame()
          if len(self.updates_iloc):
              df['forecast (prev)'] = self.update_forecasts
              df['observed'] = self.update_realized
              df['news'] = self.news
              df['impact'] = df['news'] * df['weight']
          else:
              df['forecast (prev)'] = []
              df['observed'] = []
              df['news'] = []
              df['impact'] = []
          df = df[['observed', 'forecast (prev)', 'news', 'weight', 'impact']]
          df = df.reorder_levels([2, 3, 0, 1]).sort_index()
  
          if self.impacted_variable is not None and len(df) > 0:
              df = df.loc[np.s_[:, self.impacted_variable], :]
  
          mask = np.abs(df['weight']) > self.tolerance
          return df[mask]
  
      @property
      def revision_details_by_impact(self):
          """
          Details of forecast revisions from revised data, organized by impacts
  
          Returns
          -------
          details : pd.DataFrame
              Index is as MultiIndex consisting of:
  
              - `impact date`: the date of the impact on the variable of interest
              - `impacted variable`: the variable that is being impacted
              - `revision date`: the date of the data revision, that results in
                `revision` that impacts the forecast of variables of interest
              - `revised variable`: the variable being revised, that results in
                `news` that impacts the forecast of variables of interest
  
              The columns are:
  
              - `observed (prev)`: the previous value of the observation, as it
                was given in the previous dataset
              - `revised`: the value of the revised entry, as it is observed in
                the new dataset
              - `revision`: the revision (this is `revised` - `observed (prev)`)
              - `weight`: the weight describing how the `revision` effects the
                forecast of the variable of interest
              - `impact`: the impact of the `revision` on the forecast of the
                variable of interest
  
          Notes
          -----
          This table decomposes updated forecasts of variables of interest from
          the `revision` associated with each revised datapoint from the new data
          release.
  
          This table does not summarize the impacts or show the effect of
          new datapoints. That information can be found in the
          `impacts` or `details_by_impact` tables.
  
          This form of the details table is organized so that the impacted
          dates / variables are first in the index. This is convenient for
          slicing by impacted variables / dates to view the details of data
          updates for a particular variable or date.
  
          However, since the `observed (prev)` and `revised` columns have a lot
          of duplication, printing the entire table gives a result that is less
          easy to parse than that produced by the `details_by_revision` property.
          `details_by_revision` contains the same information but is organized to
          be more convenient for displaying the entire table of detailed
          revisions. At the same time, `details_by_revision` is less convenient
          for subsetting.
  
          See Also
          --------
          details_by_revision
          details_by_impact
          impacts
          """
          weights = self.revision_weights.stack(level=[0, 1])
          df = pd.concat([
              self.revised.reindex(weights.index),
              self.revised_prev.rename('observed (prev)').reindex(weights.index),
              self.revisions.reindex(weights.index),
              weights.rename('weight'),
              (self.revisions * weights).rename('impact'),
          ], axis=1)
  
          df = df.reorder_levels([2, 3, 0, 1]).sort_index()
  
          if self.impacted_variable is not None and len(df) > 0:
              df = df.loc[np.s_[:, self.impacted_variable], :]
  
          mask = np.abs(df['weight']) > self.tolerance
          return df[mask]
  
      @property
      def details_by_update(self):
          """
          Details of forecast revisions from news, organized by updates first
  
          Returns
          -------
          details : pd.DataFrame
              Index is as MultiIndex consisting of:
  
              - `update date`: the date of the data update, that results in
                `news` that impacts the forecast of variables of interest
              - `updated variable`: the variable being updated, that results in
                `news` that impacts the forecast of variables of interest
              - `forecast (prev)`: the previous forecast of the new entry,
                based on the information available in the previous dataset
              - `observed`: the value of the new entry, as it is observed in the
                new dataset
              - `impact date`: the date of the impact on the variable of interest
              - `impacted variable`: the variable that is being impacted
  
              The columns are:
  
              - `news`: the news associated with the update (this is just the
                forecast error: `observed` - `forecast (prev)`)
              - `weight`: the weight describing how the `news` affects the
                forecast of the variable of interest
              - `impact`: the impact of the `news` on the forecast of the
                variable of interest
  
          Notes
          -----
          This table decomposes updated forecasts of variables of interest from
          the `news` associated with each updated datapoint from the new data
          release.
  
          This table does not summarize the impacts or show the effect of
          revisions. That information can be found in the `impacts` table.
  
          This form of the details table is organized so that the updated
          dates / variables are first in the index, and in this table the index
          also contains the forecasts and observed values of the updates. This is
          convenient for displaying the entire table of detailed updates because
          it allows sparsifying duplicate entries.
  
          However, since it includes forecasts and observed values in the index
          of the table, it is not convenient for subsetting by the variable of
          interest. Instead, the `details_by_impact` property is organized to
          make slicing by impacted variables / dates easy. This allows, for
          example, viewing the details of data updates on a particular variable
          or date of interest.
  
          See Also
          --------
          details_by_impact
          impacts
          """
          df = self.weights.stack(level=[0, 1]).rename('weight').to_frame()
          if len(self.updates_iloc):
              df['forecast (prev)'] = self.update_forecasts
              df['observed'] = self.update_realized
              df['news'] = self.news
              df['impact'] = df['news'] * df['weight']
          else:
              df['forecast (prev)'] = []
              df['observed'] = []
              df['news'] = []
              df['impact'] = []
          df = df[['forecast (prev)', 'observed', 'news',
                   'weight', 'impact']]
          df = df.reset_index()
          keys = ['update date', 'updated variable', 'observed',
                  'forecast (prev)', 'impact date', 'impacted variable']
          df.index = pd.MultiIndex.from_arrays([df[key] for key in keys])
          details = df.drop(keys, axis=1).sort_index()
  
          if self.impacted_variable is not None and len(df) > 0:
              details = details.loc[
                  np.s_[:, :, :, :, :, self.impacted_variable], :]
  
          mask = np.abs(details['weight']) > self.tolerance
          return details[mask]
  
      @property
      def revision_details_by_update(self):
          """
          Details of forecast revisions from revisions, organized by updates
  
          Returns
          -------
          details : pd.DataFrame
              Index is as MultiIndex consisting of:
  
              - `revision date`: the date of the data revision, that results in
                `revision` that impacts the forecast of variables of interest
              - `revised variable`: the variable being revised, that results in
                `news` that impacts the forecast of variables of interest
              - `observed (prev)`: the previous value of the observation, as it
                was given in the previous dataset
              - `revised`: the value of the revised entry, as it is observed in
                the new dataset
              - `impact date`: the date of the impact on the variable of interest
              - `impacted variable`: the variable that is being impacted
  
              The columns are:
  
              - `revision`: the revision (this is `revised` - `observed (prev)`)
              - `weight`: the weight describing how the `revision` affects the
                forecast of the variable of interest
              - `impact`: the impact of the `revision` on the forecast of the
                variable of interest
  
          Notes
          -----
          This table decomposes updated forecasts of variables of interest from
          the `revision` associated with each revised datapoint from the new data
          release.
  
          This table does not summarize the impacts or show the effect of
          revisions. That information can be found in the `impacts` table.
  
          This form of the details table is organized so that the revision
          dates / variables are first in the index, and in this table the index
          also contains the previously observed and revised values. This is
          convenient for displaying the entire table of detailed revisions
          because it allows sparsifying duplicate entries.
  
          However, since it includes previous observations and revisions in the
          index of the table, it is not convenient for subsetting by the variable
          of interest. Instead, the `revision_details_by_impact` property is
          organized to make slicing by impacted variables / dates easy. This
          allows, for example, viewing the details of data revisions on a
          particular variable or date of interest.
  
          See Also
          --------
          details_by_impact
          impacts
          """
          weights = self.revision_weights.stack(level=[0, 1])
  
          df = pd.concat([
              self.revised_prev.rename('observed (prev)').reindex(weights.index),
              self.revised.reindex(weights.index),
              self.revisions.reindex(weights.index),
              weights.rename('weight'),
              (self.revisions * weights).rename('impact'),
          ], axis=1)
  
          details = (df.set_index(['observed (prev)', 'revised'], append=True)
                       .reorder_levels([
                           'revision date', 'revised variable', 'revised',
                           'observed (prev)', 'impact date',
                           'impacted variable'])
                       .sort_index())
  
          if self.impacted_variable is not None and len(df) > 0:
              details = details.loc[
                  np.s_[:, :, :, :, :, self.impacted_variable], :]
  
          mask = np.abs(details['weight']) > self.tolerance
          return details[mask]
  
      @property
      def impacts(self):
          """
          Impacts from news and revisions on all dates / variables of interest
  
          Returns
          -------
          impacts : pd.DataFrame
              Index is as MultiIndex consisting of:
  
              - `impact date`: the date of the impact on the variable of interest
              - `impacted variable`: the variable that is being impacted
  
              The columns are:
  
              - `estimate (prev)`: the previous estimate / forecast of the
                date / variable of interest.
              - `impact of revisions`: the impact of all data revisions on
                the estimate of the date / variable of interest.
              - `impact of news`: the impact of all news on the estimate of
                the date / variable of interest.
              - `total impact`: the total impact of both revisions and news on
                the estimate of the date / variable of interest.
              - `estimate (new)`: the new estimate / forecast of the
                date / variable of interest after taking into account the effects
                of the revisions and news.
  
          Notes
          -----
          This table decomposes updated forecasts of variables of interest into
          the overall effect from revisions and news.
  
          This table does not break down the detail by the updated
          dates / variables. That information can be found in the
          `details_by_impact` `details_by_update` tables.
  
          See Also
          --------
          details_by_impact
          details_by_update
          """
          # Summary of impacts
          impacts = pd.concat([
              self.prev_impacted_forecasts.unstack().rename('estimate (prev)'),
              self.revision_impacts.unstack().rename('impact of revisions'),
              self.update_impacts.unstack().rename('impact of news'),
              self.post_impacted_forecasts.unstack().rename('estimate (new)')],
              axis=1)
          impacts['impact of revisions'] = (
              impacts['impact of revisions'].fillna(0))
          impacts['impact of news'] = (
              impacts['impact of news'].fillna(0))
          impacts['total impact'] = (impacts['impact of revisions'] +
                                     impacts['impact of news'])
          impacts = impacts.reorder_levels([1, 0]).sort_index()
          impacts.index.names = ['impact date', 'impacted variable']
          impacts = impacts[['estimate (prev)', 'impact of revisions',
                             'impact of news', 'total impact', 'estimate (new)']]
  
          if self.impacted_variable is not None:
              impacts = impacts.loc[np.s_[:, self.impacted_variable], :]
  
          tmp = np.abs(impacts[['impact of revisions', 'impact of news']])
          mask = (tmp > self.tolerance).any(axis=1)
  
          return impacts[mask]
  
      def summary_impacts(self, impact_date=None, impacted_variable=None,
                          groupby='impact date', show_revisions_columns=None,
                          sparsify=True, float_format='%.2f'):
          """
          Create summary table with detailed impacts from news; by date, variable
  
          Parameters
          ----------
          impact_date : int, str, datetime, list, array, or slice, optional
              Observation index label or slice of labels specifying particular
              impact periods to display. The impact date(s) describe the periods
              in which impacted variables were *affected* by the news. If this
              argument is given, the output table will only show this impact date
              or dates. Note that this argument is passed to the Pandas `loc`
              accessor, and so it should correspond to the labels of the model's
              index. If the model was created with data in a list or numpy array,
              then these labels will be zero-indexes observation integers.
          impacted_variable : str, list, array, or slice, optional
              Observation variable label or slice of labels specifying particular
              impacted variables to display. 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.
          groupby : {impact date, impacted date}
              The primary variable for grouping results in the impacts table. The
              default is to group by update date.
          show_revisions_columns : bool, optional
              If set to False, the impacts table will not show the impacts from
              data revisions or the total impacts. Default is to show the
              revisions and totals columns if any revisions were made and
              otherwise to hide them.
          sparsify : bool, optional, default True
              Set to False for the table to include every one of the multiindex
              keys at each row.
          float_format : str, optional
              Formatter format string syntax for converting numbers to strings.
              Default is '%.2f'.
  
          Returns
          -------
          impacts_table : SimpleTable
              Table describing total impacts from both revisions and news. See
              the documentation for the `impacts` attribute for more details
              about the index and columns.
  
          See Also
          --------
          impacts
          """
          # Squeeze for univariate models
          if impacted_variable is None and self.k_endog == 1:
              impacted_variable = self.endog_names[0]
  
          # Default is to only show the revisions columns if there were any
          # revisions (otherwise it would just be a column of zeros)
          if show_revisions_columns is None:
              show_revisions_columns = len(self.revisions_iloc) > 0
  
          # Select only the variables / dates of interest
          s = list(np.s_[:, :])
          if impact_date is not None:
              s[0] = np.s_[impact_date]
          if impacted_variable is not None:
              s[1] = np.s_[impacted_variable]
          s = tuple(s)
          impacts = self.impacts.loc[s, :]
  
          # Make the first index level the groupby level
          groupby = groupby.lower()
          if groupby in ['impacted variable', 'impacted_variable']:
              impacts.index = impacts.index.swaplevel(1, 0)
          elif groupby not in ['impact date', 'impact_date']:
              raise ValueError('Invalid groupby for impacts table. Valid options'
                               ' are "impact date" or "impacted variable".'
                               f'Got "{groupby}".')
          impacts = impacts.sort_index()
  
          # Drop the non-groupby level if there's only one value
          tmp_index = impacts.index.remove_unused_levels()
          k_vars = len(tmp_index.levels[1])
          removed_level = None
          if sparsify and k_vars == 1:
              name = tmp_index.names[1]
              value = tmp_index.levels[1][0]
              removed_level = f'{name} = {value}'
              impacts.index = tmp_index.droplevel(1)
              impacts = impacts.applymap(
                  lambda num: '' if pd.isnull(num) else float_format % num)
              impacts = impacts.reset_index()
              impacts.iloc[:, 0] = impacts.iloc[:, 0].map(str)
          else:
              impacts = impacts.reset_index()
              impacts.iloc[:, :2] = impacts.iloc[:, :2].applymap(str)
              impacts.iloc[:, 2:] = impacts.iloc[:, 2:].applymap(
                  lambda num: '' if pd.isnull(num) else float_format % num)
  
          # Sparsify the groupby column
          if sparsify and groupby in impacts:
              mask = impacts[groupby] == impacts[groupby].shift(1)
              tmp = impacts.loc[mask, groupby]
              if len(tmp) > 0:
                  impacts.loc[mask, groupby] = ''
  
          # Drop revisions and totals columns if applicable
          if not show_revisions_columns:
              impacts.drop(['impact of revisions', 'total impact'], axis=1,
                           inplace=True)
  
          params_data = impacts.values
          params_header = impacts.columns.tolist()
          params_stubs = None
  
          title = 'Impacts'
          if removed_level is not None:
              join = 'on' if groupby == 'date' else 'for'
              title += f' {join} [{removed_level}]'
          impacts_table = SimpleTable(
              params_data, params_header, params_stubs,
              txt_fmt=fmt_params, title=title)
  
          return impacts_table
  
      def summary_details(self, source='news', impact_date=None,
                          impacted_variable=None, update_date=None,
                          updated_variable=None, groupby='update date',
                          sparsify=True, float_format='%.2f',
                          multiple_tables=False):
          """
          Create summary table with detailed impacts; by date, variable
  
          Parameters
          ----------
          source : {news, revisions}
              The source of impacts to summarize. Default is "news".
          impact_date : int, str, datetime, list, array, or slice, optional
              Observation index label or slice of labels specifying particular
              impact periods to display. The impact date(s) describe the periods
              in which impacted variables were *affected* by the news. If this
              argument is given, the output table will only show this impact date
              or dates. Note that this argument is passed to the Pandas `loc`
              accessor, and so it should correspond to the labels of the model's
              index. If the model was created with data in a list or numpy array,
              then these labels will be zero-indexes observation integers.
          impacted_variable : str, list, array, or slice, optional
              Observation variable label or slice of labels specifying particular
              impacted variables to display. 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.
          update_date : int, str, datetime, list, array, or slice, optional
              Observation index label or slice of labels specifying particular
              updated periods to display. The updated date(s) describe the
              periods in which the new data points were available that generated
              the news). See the note on `impact_date` for details about what
              these labels are.
          updated_variable : str, list, array, or slice, optional
              Observation variable label or slice of labels specifying particular
              updated variables to display. The updated 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.
          groupby : {update date, updated date, impact date, impacted date}
              The primary variable for grouping results in the details table. The
              default is to group by update date.
          sparsify : bool, optional, default True
              Set to False for the table to include every one of the multiindex
              keys at each row.
          float_format : str, optional
              Formatter format string syntax for converting numbers to strings.
              Default is '%.2f'.
          multiple_tables : bool, optional
              If set to True, this function will return a list of tables, one
              table for each of the unique `groupby` levels. Default is False,
              in which case this function returns a single table.
  
          Returns
          -------
          details_table : SimpleTable or list of SimpleTable
              Table or list of tables describing how the news from each update
              (i.e. news from a particular variable / date) translates into
              changes to the forecasts of each impacted variable variable / date.
  
              This table contains information about the updates and about the
              impacts. Updates are newly observed datapoints that were not
              available in the previous results set. Each update leads to news,
              and the news may cause changes in the forecasts of the impacted
              variables. The amount that a particular piece of news (from an
              update to some variable at some date) impacts a variable at some
              date depends on weights that can be computed from the model
              results.
  
              The data contained in this table that refer to updates are:
  
              - `update date` : The date at which a new datapoint was added.
              - `updated variable` : The variable for which a new datapoint was
                added.
              - `forecast (prev)` : The value that had been forecast by the
                previous model for the given updated variable and date.
              - `observed` : The observed value of the new datapoint.
              - `news` : The news is the difference between the observed value
                and the previously forecast value for a given updated variable
                and date.
  
              The data contained in this table that refer to impacts are:
  
              - `impact date` : A date associated with an impact.
              - `impacted variable` : A variable that was impacted by the news.
              - `weight` : The weight of news from a given `update date` and
                `update variable` on a given `impacted variable` at a given
                `impact date`.
              - `impact` : The revision to the smoothed estimate / forecast of
                the impacted variable at the impact date based specifically on
                the news generated by the `updated variable` at the
                `update date`.
  
          See Also
          --------
          details_by_impact
          details_by_update
          """
          # Squeeze for univariate models
          if self.k_endog == 1:
              if impacted_variable is None:
                  impacted_variable = self.endog_names[0]
              if updated_variable is None:
                  updated_variable = self.endog_names[0]
  
          # Select only the variables / dates of interest
          s = list(np.s_[:, :, :, :, :, :])
          if impact_date is not None:
              s[0] = np.s_[impact_date]
          if impacted_variable is not None:
              s[1] = np.s_[impacted_variable]
          if update_date is not None:
              s[2] = np.s_[update_date]
          if updated_variable is not None:
              s[3] = np.s_[updated_variable]
          s = tuple(s)
  
          if source == 'news':
              details = self.details_by_impact.loc[s, :]
              columns = {
                  'current': 'observed',
                  'prev': 'forecast (prev)',
                  'update date': 'update date',
                  'updated variable': 'updated variable',
                  'news': 'news',
              }
          elif source == 'revisions':
              details = self.revision_details_by_impact.loc[s, :]
              columns = {
                  'current': 'revised',
                  'prev': 'observed (prev)',
                  'update date': 'revision date',
                  'updated variable': 'revised variable',
                  'news': 'revision',
              }
          else:
              raise ValueError(f'Invalid `source`: {source}. Must be "news" or'
                               ' "impacts".')
  
          # Make the first index level the groupby level
          groupby = groupby.lower().replace('_', ' ')
          groupby_overall = 'impact'
          levels_order = [0, 1, 2, 3]
          if groupby == 'update date':
              levels_order = [2, 3, 0, 1]
              groupby_overall = 'update'
          elif groupby == 'updated variable':
              levels_order = [3, 2, 1, 0]
              groupby_overall = 'update'
          elif groupby == 'impacted variable':
              levels_order = [1, 0, 3, 2]
          elif groupby != 'impact date':
              raise ValueError('Invalid groupby for details table. Valid options'
                               ' are "update date", "updated variable",'
                               ' "impact date",or "impacted variable".'
                               f' Got "{groupby}".')
          details.index = (details.index.reorder_levels(levels_order)
                                        .remove_unused_levels())
          details = details.sort_index()
  
          # If our overall group-by is `update`, move forecast (prev) and
          # observed into the index
          base_levels = [0, 1, 2, 3]
          if groupby_overall == 'update':
              details.set_index([columns['current'], columns['prev']],
                                append=True, inplace=True)
              details.index = details.index.reorder_levels([0, 1, 4, 5, 2, 3])
              base_levels = [0, 1, 4, 5]
  
          # Drop the non-groupby levels if there's only one value
          tmp_index = details.index.remove_unused_levels()
          n_levels = len(tmp_index.levels)
          k_level_values = [len(tmp_index.levels[i]) for i in range(n_levels)]
          removed_levels = []
          if sparsify:
              for i in sorted(base_levels)[::-1][:-1]:
                  if k_level_values[i] == 1:
                      name = tmp_index.names[i]
                      value = tmp_index.levels[i][0]
                      can_drop = (
                          (name == columns['update date']
                              and update_date is not None) or
                          (name == columns['updated variable']
                              and updated_variable is not None) or
                          (name == 'impact date'
                              and impact_date is not None) or
                          (name == 'impacted variable'
                              and (impacted_variable is not None or
                                   self.impacted_variable is not None)))
                      if can_drop or not multiple_tables:
                          removed_levels.insert(0, f'{name} = {value}')
                          details.index = tmp_index = tmp_index.droplevel(i)
  
          # Move everything to columns
          details = details.reset_index()
  
          # Function for formatting numbers
          def str_format(num, mark_ones=False, mark_zeroes=False):
              if pd.isnull(num):
                  out = ''
              elif mark_ones and np.abs(1 - num) < self.tolerance:
                  out = '1.0'
              elif mark_zeroes and np.abs(num) < self.tolerance:
                  out = '0'
              else:
                  out = float_format % num
              return out
  
          # Function to create the table
          def create_table(details, removed_levels):
              # Convert everything to strings
              for key in [columns['current'], columns['prev'], columns['news'],
                          'weight', 'impact']:
                  if key in details:
                      args = (
                          # mark_ones
                          True if key in ['weight'] else False,
                          # mark_zeroes
                          True if key in ['weight', 'impact'] else False)
                      details[key] = details[key].apply(str_format, args=args)
              for key in [columns['update date'], 'impact date']:
                  if key in details:
                      details[key] = details[key].apply(str)
  
              # Sparsify index columns
              if sparsify:
                  sparsify_cols = [columns['update date'],
                                   columns['updated variable'], 'impact date',
                                   'impacted variable']
                  data_cols = [columns['current'], columns['prev']]
                  if groupby_overall == 'update':
                      # Put data columns first, since we need to do an additional
                      # check based on the other columns before sparsifying
                      sparsify_cols = data_cols + sparsify_cols
  
                  for key in sparsify_cols:
                      if key in details:
                          mask = details[key] == details[key].shift(1)
                          if key in data_cols:
                              if columns['update date'] in details:
                                  tmp = details[columns['update date']]
                                  mask &= tmp == tmp.shift(1)
                              if columns['updated variable'] in details:
                                  tmp = details[columns['updated variable']]
                                  mask &= tmp == tmp.shift(1)
                          details.loc[mask, key] = ''
  
              params_data = details.values
              params_header = [str(x) for x in details.columns.tolist()]
              params_stubs = None
  
              title = f"Details of {source}"
              if len(removed_levels):
                  title += ' for [' + ', '.join(removed_levels) + ']'
              return SimpleTable(params_data, params_header, params_stubs,
                                 txt_fmt=fmt_params, title=title)
  
          if multiple_tables:
              details_table = []
              for item in details[groupby].unique():
                  mask = details[groupby] == item
                  item_details = details[mask].drop(groupby, axis=1)
                  item_removed_levels = [f'{groupby} = {item}'] + removed_levels
                  details_table.append(create_table(item_details,
                                                    item_removed_levels))
          else:
              details_table = create_table(details, removed_levels)
  
          return details_table
  
      def summary_revisions(self, sparsify=True):
          """
          Create summary table showing revisions to the previous results' data
  
          Parameters
          ----------
          sparsify : bool, optional, default True
              Set to False for the table to include every one of the multiindex
              keys at each row.
  
          Returns
          -------
          revisions_table : SimpleTable
              Table showing revisions to the previous results' data. Columns are:
  
              - `revision date` : date associated with a revised data point
              - `revised variable` : variable that was revised at `revision date`
              - `observed (prev)` : the observed value prior to the revision
              - `revised` : the new value after the revision
              - `revision` : the new value after the revision
          """
          data = pd.merge(
              self.data_revisions, self.revisions, left_index=True,
              right_index=True).sort_index().reset_index()
          data[['revision date', 'revised variable']] = (
              data[['revision date', 'revised variable']].applymap(str))
          data.iloc[:, 2:] = data.iloc[:, 2:].applymap(
              lambda num: '' if pd.isnull(num) else '%.2f' % num)
  
          # Sparsify the date column
          if sparsify:
              mask = data['revision date'] == data['revision date'].shift(1)
              data.loc[mask, 'revision date'] = ''
  
          params_data = data.values
          params_header = data.columns.tolist()
          params_stubs = None
  
          title = 'Revisions to dataset:'
          revisions_table = SimpleTable(
              params_data, params_header, params_stubs,
              txt_fmt=fmt_params, title=title)
  
          return revisions_table
  
      def summary_news(self, sparsify=True):
          """
          Create summary table showing news from new data since previous results
  
          Parameters
          ----------
          sparsify : bool, optional, default True
              Set to False for the table to include every one of the multiindex
              keys at each row.
  
          Returns
          -------
          updates_table : SimpleTable
              Table showing new datapoints that were not in the previous results'
              data. Columns are:
  
              - `update date` : date associated with a new data point.
              - `updated variable` : variable for which new data was added at
                `update date`.
              - `forecast (prev)` : the forecast value for the updated variable
                at the update date in the previous results object (i.e. prior to
                the data being available).
              - `observed` : the observed value of the new datapoint.
  
          See Also
          --------
          data_updates
          """
          data = pd.merge(
              self.data_updates, self.news, left_index=True,
              right_index=True).sort_index().reset_index()
          data[['update date', 'updated variable']] = (
              data[['update date', 'updated variable']].applymap(str))
          data.iloc[:, 2:] = data.iloc[:, 2:].applymap(
              lambda num: '' if pd.isnull(num) else '%.2f' % num)
  
          # Sparsify the date column
          if sparsify:
              mask = data['update date'] == data['update date'].shift(1)
              data.loc[mask, 'update date'] = ''
  
          params_data = data.values
          params_header = data.columns.tolist()
          params_stubs = None
  
          title = 'News from updated observations:'
          updates_table = SimpleTable(
              params_data, params_header, params_stubs,
              txt_fmt=fmt_params, title=title)
  
          return updates_table
  
      def summary(self, impact_date=None, impacted_variable=None,
                  update_date=None, updated_variable=None,
                  revision_date=None, revised_variable=None,
                  impacts_groupby='impact date', details_groupby='update date',
                  show_revisions_columns=None, sparsify=True,
                  include_details_tables=None, include_revisions_tables=False,
                  float_format='%.2f'):
          """
          Create summary tables describing news and impacts
  
          Parameters
          ----------
          impact_date : int, str, datetime, list, array, or slice, optional
              Observation index label or slice of labels specifying particular
              impact periods to display. The impact date(s) describe the periods
              in which impacted variables were *affected* by the news. If this
              argument is given, the impact and details tables will only show
              this impact date or dates. Note that this argument is passed to the
              Pandas `loc` accessor, and so it should correspond to the labels of
              the model's index. If the model was created with data in a list or
              numpy array, then these labels will be zero-indexes observation
              integers.
          impacted_variable : str, list, array, or slice, optional
              Observation variable label or slice of labels specifying particular
              impacted variables to display. 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.
          update_date : int, str, datetime, list, array, or slice, optional
              Observation index label or slice of labels specifying particular
              updated periods to display. The updated date(s) describe the
              periods in which the new data points were available that generated
              the news). See the note on `impact_date` for details about what
              these labels are.
          updated_variable : str, list, array, or slice, optional
              Observation variable label or slice of labels specifying particular
              updated variables to display. The updated variable(s) describe the
              variables that newly added in the updated dataset and which
              generated the news. If you do not know the labels for the
              variables, check the `endog_names` attribute of the model instance.
          revision_date : int, str, datetime, list, array, or slice, optional
              Observation index label or slice of labels specifying particular
              revision periods to display. The revision date(s) describe the
              periods in which the data points were revised. See the note on
              `impact_date` for details about what these labels are.
          revised_variable : str, list, array, or slice, optional
              Observation variable label or slice of labels specifying particular
              revised variables to display. The updated variable(s) describe the
              variables that were *revised*. If you do not know the labels for
              the variables, check the `endog_names` attribute of the model
              instance.
          impacts_groupby : {impact date, impacted date}
              The primary variable for grouping results in the impacts table. The
              default is to group by update date.
          details_groupby : str
              One of "update date", "updated date", "impact date", or
              "impacted date". The primary variable for grouping results in the
              details table. Only used if the details tables are included. The
              default is to group by update date.
          show_revisions_columns : bool, optional
              If set to False, the impacts table will not show the impacts from
              data revisions or the total impacts. Default is to show the
              revisions and totals columns if any revisions were made and
              otherwise to hide them.
          sparsify : bool, optional, default True
              Set to False for the table to include every one of the multiindex
              keys at each row.
          include_details_tables : bool, optional
              If set to True, the summary will show tables describing the details
              of how news from specific updates translate into specific impacts.
              These tables can be very long, particularly in cases where there
              were many updates and in multivariate models. The default is to
              show detailed tables only for univariate models.
          include_revisions_tables : bool, optional
              If set to True, the summary will show tables describing the
              revisions and updates that lead to impacts on variables of
              interest.
          float_format : str, optional
              Formatter format string syntax for converting numbers to strings.
              Default is '%.2f'.
  
          Returns
          -------
          summary_tables : Summary
              Summary tables describing news and impacts. Basic tables include:
  
              - A table with general information about the sample.
              - A table describing the impacts of revisions and news.
              - Tables describing revisions in the dataset since the previous
                results set (unless `include_revisions_tables=False`).
  
              In univariate models or if `include_details_tables=True`, one or
              more tables will additionally be included describing the details
              of how news from specific updates translate into specific impacts.
  
          See Also
          --------
          summary_impacts
          summary_details
          summary_revisions
          summary_updates
          """
          # Default for include_details_tables
          if include_details_tables is None:
              include_details_tables = (self.k_endog == 1)
  
          # Model specification results
          model = self.model.model
          title = 'News'
  
          def get_sample(model):
              if model._index_dates:
                  mask = ~np.isnan(model.endog).all(axis=1)
                  ix = model._index[mask]
                  d = ix[0]
                  sample = ['%s' % d]
                  d = ix[-1]
                  sample += ['- ' + '%s' % d]
              else:
                  sample = [str(0), ' - ' + str(model.nobs)]
  
              return sample
          previous_sample = get_sample(self.previous.model)
          revised_sample = get_sample(self.updated.model)
  
          # Standardize the model name as a list of str
          model_name = model.__class__.__name__
  
          # Top summary table
          top_left = [('Model:', [model_name]),
                      ('Date:', None),
                      ('Time:', None)]
          if self.state_index is not None:
              k_states_used = len(self.state_index)
              if k_states_used != self.model.model.k_states:
                  top_left.append(('# of included states:', [k_states_used]))
  
          top_right = [
              ('Original sample:', [previous_sample[0]]),
              ('', [previous_sample[1]]),
              ('Update through:', [revised_sample[1][2:]]),
              ('# of revisions:', [len(self.revisions_ix)]),
              ('# of new datapoints:', [len(self.updates_ix)])]
  
          summary = Summary()
          self.model.endog_names = self.model.model.endog_names
          summary.add_table_2cols(self, gleft=top_left, gright=top_right,
                                  title=title)
          table_ix = 1
  
          # Impact table
          summary.tables.insert(table_ix, self.summary_impacts(
              impact_date=impact_date, impacted_variable=impacted_variable,
              groupby=impacts_groupby,
              show_revisions_columns=show_revisions_columns, sparsify=sparsify,
              float_format=float_format))
          table_ix += 1
  
          # News table
          if len(self.updates_iloc) > 0:
              summary.tables.insert(
                  table_ix, self.summary_news(sparsify=sparsify))
              table_ix += 1
  
          # Detail tables
          multiple_tables = (self.k_endog > 1)
          details_tables = self.summary_details(
              source='news',
              impact_date=impact_date, impacted_variable=impacted_variable,
              update_date=update_date, updated_variable=updated_variable,
              groupby=details_groupby, sparsify=sparsify,
              float_format=float_format, multiple_tables=multiple_tables)
          if not multiple_tables:
              details_tables = [details_tables]
  
          if include_details_tables:
              for table in details_tables:
                  summary.tables.insert(table_ix, table)
                  table_ix += 1
  
          # Revisions
          if include_revisions_tables and len(self.revisions_iloc) > 0:
              summary.tables.insert(
                  table_ix, self.summary_revisions(sparsify=sparsify))
              table_ix += 1
  
              # Revision detail tables
              revision_details_tables = self.summary_details(
                  source='revisions',
                  impact_date=impact_date, impacted_variable=impacted_variable,
                  update_date=revision_date, updated_variable=revised_variable,
                  groupby=details_groupby, sparsify=sparsify,
                  float_format=float_format, multiple_tables=multiple_tables)
              if not multiple_tables:
                  revision_details_tables = [revision_details_tables]
  
              if include_details_tables:
                  for table in revision_details_tables:
                      summary.tables.insert(table_ix, table)
                      table_ix += 1
  
          return summary
  
      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)
  
      def get_impacts(self, groupby=None, include_revisions=True,
                      include_updates=True):
          details = self.get_details(include_revisions=include_revisions,
                                     include_updates=include_updates)
  
          impacts = details['impact'].unstack(['impact date',
                                               'impacted variable'])
  
          if groupby is not None:
              impacts = (impacts.unstack('update date')
                                .groupby(groupby).sum(min_count=1)
                                .stack('update date')
                                .swaplevel()
                                .sort_index())
  
          return impacts
  