Mixed Frequency Structural Vector Auto-Regressive Models. (18th May 2015)

• Record Type: Journal Article
• Authors: Foroni, Claudia; Marcellino, Massimiliano
• Is Part Of: Journal of the Royal Statistical Society. Volume 179:Number 2(2016:Apr.)
• Page Start: 403
• Page End: 425
• Publication Date: 2015-05-18
• Subjects: Estimation -- Identification -- Impulse response function -- Mixed frequency data -- Structural vector auto-regression -- Temporal aggregation; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12120
• Languages: English
• ISSNs: 0964-1998

Abstract

Summary: A mismatch between the timescale of a structural vector auto-regressive model and that of the time series data used for its estimation can have serious consequences for identification, estimation and interpretation of the impulse response functions. However, the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identification issues and yield more reliable responses to shocks. The problems and possible remedy are illustrated analytically and with both simulated and actual data.