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Testing for News and Noise in Non-Stationary Time Series Subject to Multiple Historical Revisions

Before being considered definitive, data currently produced by statistical agencies undergo a recurrent revision process resulting in different releases of the same phenomenon. The collection of all these vintages is referred to as a real-time data set. Economists and econometricians have realized the importance of this type of information for economic modeling and forecasting.

This paper focuses on testing non-stationary data for forecastability, i.e., whether revisions reduce noise or are news. To deal with historical revisions which affect the whole vintage of time series due to redefinitions, methodological innovations etc., we employ the recently developed impulse indicator saturation approach, which involves potentially adding an indicator dummy for each observation to the model. We illustrate our procedures with the U.S. Real Gross National Product series from ALFRED and find that revisions to this series neither reduce noise nor can be considered as news.
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