- NewsResults.summary_impacts(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
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.
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.
The primary variable for grouping results in the impacts table. The default is to group by update date.
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.
Set to False for the table to include every one of the multiindex keys at each row.
Formatter format string syntax for converting numbers to strings. Default is ‘%.2f’.
Table describing total impacts from both revisions and news. See the documentation for the impacts attribute for more details about the index and columns.