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Testing for news and noise in non-stationary time series subject to multiple historical revisions

Alain Hecq (), Jan Jacobs () and Michalis P. Stamatogiannis

Journal of Macroeconomics, 2019, vol. 60, issue C, 396-407

Abstract: This paper focuses on testing non-stationary real-time data for forecastability, i.e., whether data revisions reduce noise or are news, by putting data releases in vector-error correction forms. 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 GNP/GDP series of the Federal Reserve Bank of Philadelphia and find that revisions to this series neither reduce noise nor can be considered as news.

Keywords: Data revision; Cointegration; News-noise tests; Outlier detection (search for similar items in EconPapers)
JEL-codes: C32 C82 E01 (search for similar items in EconPapers)
Date: 2019
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Related works:
Working Paper: Testing for News and Noise in Non-Stationary Time Series Subject to Multiple Historical Revisions (2016) Downloads
Working Paper: Testing for news and noise in non-stationary time series subject to multiple historical revisions (2016) Downloads
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