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

Hecq, Alain, Jacobs, Jan PAM and Stamatogiannis, Michalis P (2019) Testing for news and noise in non-stationary time series subject to multiple historical revisions [Scholarly Edition]

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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.

Item Type:	Scholarly Edition
Uncontrolled Keywords:	Data revision, Cointegration, News-noise tests, Outlier detection
Divisions:	Faculty of Humanities & Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	23 Jun 2022 09:56
Last Modified:	16 Jun 2026 07:03
DOI:	10.1016/j.jmacro.2019.03.003
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3157036
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