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.
- c32 - "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
- c82 - "Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access"
- e01 - "Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts"
- Data revision
- News-noise tests
- Outlier detection
- WEAK EXOGENEITY
Hecq, A., Jacobs, J. P. A. M., & Stamatogiannis, M. P. (2019). Testing for news and noise in non-stationary time series subject to multiple historical revisions. Journal of Macroeconomics, 60, 396-407. https://doi.org/10.1016/j.jmacro.2019.03.003