statsmodels.tsa.statespace.kalman_smoother.SmootherResults.news

SmootherResults.news(previous, t=None, start=None, end=None, revised=None, design=None, state_index=None)[source]

Compute the news and impacts associated with a data release

Parameters
previousSmootherResults

Prior results object relative to which to compute the news. This results object must have identical state space representation for the prior sample period so that the only difference is that this results object has updates to the observed data.

tint, optional

A specific period for which to compute the news. Cannot be used in combination with start or end.

startint, optional

The start of the interval (inclusive) of news to compute. Cannot be used in combination with the t argument. Default is the last period of the sample (nobs - 1).

endint, optional

The end of the interval (exclusive) of news to compute. Note that since it is an exclusive endpoint, the returned news do not include the value at this index. Cannot be used in combination with the t argument.

designarray, optional

Design matrix for the period t in time-varying models. If this model has a time-varying design matrix, and the argument t is out of this model’s sample, then a new design matrix for period t must be provided. Unused otherwise.

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.

Returns
news_resultsSimpleNamespace

News and impacts associated with a data release. Includes the following attributes:

  • update_impacts: update to forecasts of impacted variables from the news. It is equivalent to E[y^i | post] - E[y^i | revision], where y^i are the variables of interest. In [1], this is described as “revision” in equation (17).

  • revision_impacts: update to forecasts of variables impacted variables from data revisions. It is E[y^i | revision] - E[y^i | previous], and does not have a specific notation in [1], since there for simplicity they assume that there are no revisions.

  • news: the unexpected component of the updated data. Denoted I = y^u - E[y^u | previous], where y^u are the data points that were newly incorporated in a data release (but not including revisions to data points that already existed in the previous release). In [1], this is described as “news” in equation (17).

  • revisions

  • gain: the gain matrix associated with the “Kalman-like” update from the news, E[y I’] E[I I’]^{-1}. In [1], this can be found in the equation For E[y_{k,t_k} mid I_{v+1}] in the middle of page 17.

  • revision_weights

  • update_forecasts: forecasts of the updated periods used to construct the news, E[y^u | previous].

  • update_realized: realizations of the updated periods used to construct the news, y^u.

  • revised_prev

  • revised

  • prev_impacted_forecasts: previous forecast of the periods of interest, E[y^i | previous].

  • post_impacted_forecasts: forecast of the periods of interest after taking into account both revisions and updates, E[y^i | post].

  • revision_results: results object that updates the previous results to take into account data revisions.

  • revisions_ix: list of (t, i) positions of revisions in endog

  • updates_ix: list of (t, i) positions of updates to endog

Notes

This method computes the effect of new data (e.g. from a new data release) on smoothed forecasts produced by a state space model, as described in [1]. It also computes the effect of revised data on smoothed forecasts.

References

1(1,2,3,4,5)

Bańbura, Marta and Modugno, Michele. 2010. “Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data.” No 1189, Working Paper Series, European Central Bank. https://EconPapers.repec.org/RePEc:ecb:ecbwps:20101189.

2

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.