Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Multivariate singular spectrum analysis for forecasting revisions to real-time data

Patterson, Kerry, Hassani, Hossein, Heravi, Saeed ORCID: and Zhigljavsky, Anatoly Alexandrovich ORCID: 2011. Multivariate singular spectrum analysis for forecasting revisions to real-time data. Journal of Applied Statistics 38 (10) , pp. 2183-2211. 10.1080/02664763.2010.545371

Full text not available from this repository.


Real-time data on national accounts statistics typically undergo an extensive revision process, leading to multiple vintages on the same generic variable. The time between the publication of the initial and final data is a lengthy one and raises the question of how to model and forecast the final vintage of data – an issue that dates from seminal articles by Mankiw et al., Mankiw and Shapiro and Nordhaus. To solve this problem, we develop the non-parametric method of multivariate singular spectrum analysis (MSSA) for multi-vintage data. MSSA is much more flexible than the standard methods of modelling that involve at least one of the restrictive assumptions of linearity, normality and stationarity. The benefits are illustrated with data on the UK index of industrial production: neither the preliminary vintages nor the competing models are as accurate as the forecasts using MSSA.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Q Science > QA Mathematics
Uncontrolled Keywords: Non-parametric methods ; Data revisions ; Trajectory matrix ; Reconstruction ; Hankelisation ; Recurrence formula ; Forecasting
Publisher: Taylor & Francis
ISSN: 0266-4763
Last Modified: 19 Oct 2022 10:01

Citation Data

Cited 37 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item