statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.news¶

ExponentialSmoothingResults.news(comparison, impact_date=None, impacted_variable=None, start=None, end=None, periods=None, exog=None, comparison_type=None, revisions_details_start=False, state_index=None, return_raw=False, tolerance=1e-10, **kwargs)¶

Compute impacts from updated data (news and revisions)

Parameters:¶

comparisonarray_like or MLEResults

An updated dataset with updated and/or revised data from which the news can be computed, or an updated or previous results object to use in computing the news.

impact_dateint, str, or datetime, optional

A single specific period of impacts from news and revisions to compute. Can also be a date string to parse or a datetime type. This argument cannot be used in combination with start, end, or periods. Default is the first out-of-sample observation.

impacted_variablestr, list, array, or slice, optional

Observation variable label or slice of labels specifying that only specific impacted variables should be shown in the News 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.

startint, str, or datetime, optional

The first period of impacts from news and revisions to compute. Can also be a date string to parse or a datetime type. Default is the first out-of-sample observation.

endint, str, or datetime, optional

The last period of impacts from news and revisions to compute. Can also be a date string to parse or a datetime type. Default is the first out-of-sample observation.

periodsint, optional

The number of periods of impacts from news and revisions to compute.

exogarray_like, optional

Array of exogenous regressors for the out-of-sample period, if applicable.

comparison_type{None, ‘previous’, ‘updated’}

This denotes whether the comparison argument represents a previous results object or dataset or an updated results object or dataset. If not specified, then an attempt is made to determine the comparison type.

revisions_details_startbool, int, str, or datetime, optional

The period at which to beging computing the detailed impacts of data revisions. Any revisions prior to this period will have their impacts grouped together. If a negative integer, interpreted as an offset from the end of the dataset. If set to True, detailed impacts are computed for all revisions, while if set to False, all revisions are grouped together. Default is False. Note that for large models, setting this to be near the beginning of the sample can cause this function to be slow.

state_indexarray_like, optional

An optional index specifying a subset of states to use when constructing the impacts of revisions and news. For example, if state_index=[0, 1] is passed, then only the impacts to the observed variables arising from the impacts to the first two states will be returned. Default is to use all states.

return_rawbool, optional

Whether or not to return only the specific output or a full results object. Default is to return a full results object.

tolerancefloat, optional

The numerical threshold for determining zero impact. Default is that any impact less than 1e-10 is assumed to be zero.

Returns:¶

NewsResults

Impacts of data revisions and news on estimates

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.