statsmodels.tsa.statespace.news.NewsResults¶
-
class statsmodels.tsa.statespace.news.NewsResults(news_results, model, updated, previous, impacted_variable=
None, tolerance=1e-10, row_labels=None)[source]¶ 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.
- total_impacts¶
Updates to forecasts of impacted variables from both news and data revisions, E[y^i | post] - E[y^i | previous].
- Type:¶
pd.DataFrame
- update_impacts¶
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.
- Type:¶
pd.DataFrame
- revision_impacts¶
Updates to forecasts of impacted variables from all data revisions, E[y^i | revisions] - E[y^i | previous].
- Type:¶
pd.DataFrame
- news¶
The unexpected component of the updated data, E[y^u | post] - E[y^u | revisions] where y^u are the updated variables.
- Type:¶
pd.DataFrame
- revisions¶
The revisions between the current and previously observed data, for revisions for which detailed impacts were computed.
- Type:¶
pd.DataFrame
- revisions_all¶
The revisions between the current and previously observed data, y^r_{revised} - y^r_{previous} where y^r are the revised variables.
- Type:¶
pd.DataFrame
- revision_weights¶
Weights describing the effect of revisions on variables of interest, for revisions for which detailed impacts were computed.
- Type:¶
pd.DataFrame
- revision_weights_all¶
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.
- Type:¶
pd.DataFrame
- update_forecasts¶
Forecasts based on the previous dataset of the variables that were updated, E[y^u | previous].
- Type:¶
pd.DataFrame
- update_realized¶
Actual observed data associated with the variables that were updated, y^u
- Type:¶
pd.DataFrame
- revisions_details_start¶
Integer index of first period in which detailed revision impacts were computed.
- revision_detailed_impacts¶
Updates to forecasts of impacted variables from data revisions with detailed impacts, E[y^i | revisions] - E[y^i | grouped revisions].
- Type:¶
pd.DataFrame
- revision_grouped_impacts¶
Updates to forecasts of impacted variables from data revisions that were grouped together, E[y^i | grouped revisions] - E[y^i | previous].
- Type:¶
pd.DataFrame
- revised_prev¶
Previously observed data associated with the variables that were revised, for revisions for which detailed impacts were computed.
- Type:¶
pd.DataFrame
- revised_prev_all¶
Previously observed data associated with the variables that were revised, y^r_{previous}
- Type:¶
pd.DataFrame
- revised¶
Currently observed data associated with the variables that were revised, for revisions for which detailed impacts were computed.
- Type:¶
pd.DataFrame
- revised_all¶
Currently observed data associated with the variables that were revised, y^r_{revised}
- Type:¶
pd.DataFrame
- prev_impacted_forecasts¶
Previous forecast of the variables of interest, E[y^i | previous].
- Type:¶
pd.DataFrame
- post_impacted_forecasts¶
Forecast of the variables of interest after taking into account both revisions and updates, E[y^i | post].
- Type:¶
pd.DataFrame
- revisions_iloc_detailed¶
The integer locations of the data revisions in the dataset for which detailed impacts were computed.
- Type:¶
pd.DataFrame
- revisions_ix_detailed¶
The label-based locations of the data revisions in the dataset for which detailed impacts were computed.
- Type:¶
pd.DataFrame
References
Methods
get_details([include_revisions, include_updates])get_impacts([groupby, include_revisions, ...])summary([impact_date, impacted_variable, ...])Create summary tables describing news and impacts
summary_details([source, impact_date, ...])Create summary table with detailed impacts; by date, variable
summary_impacts([impact_date, ...])Create summary table with detailed impacts from news; by date, variable
summary_news([sparsify])Create summary table showing news from new data since previous results
summary_revisions([sparsify])Create summary table showing revisions to the previous results' data
Properties
Revisions to data points that existed in the previous dataset
Updated data; new entries that did not exist in the previous dataset
Details of forecast revisions from news, organized by impacts first
Details of forecast revisions from news, organized by updates first
Impacts from news and revisions on all dates / variables of interest
Details of forecast revisions from revised data, organized by impacts
Details of forecast revisions from revisions, organized by updates