- RecursiveLSResults.news(comparison, impact_date=None, impacted_variable=None, start=None, end=None, periods=None, exog=None, comparison_type=None, return_raw=False, tolerance=1e-10, **kwargs)¶
Compute impacts from updated data (news and revisions)
- comparisonarray_like or
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.
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.
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.
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.
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.
The number of periods of impacts from news and revisions to compute.
Array of exogenous regressors for the out-of-sample period, if applicable.
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.
Whether or not to return only the specific output or a full results object. Default is to return a full results object.
The numerical threshold for determining zero impact. Default is that any impact less than 1e-10 is assumed to be zero.
- comparisonarray_like or
Impacts of data revisions and news on estimates
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.
Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin. “Nowcasting.” The Oxford Handbook of Economic Forecasting. July 8, 2011.
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.