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

Patterson, Kerry, Hassani, Hossein, Heravi, Saeed ORCID: https://orcid.org/0000-0002-0198-764X and Zhigljavsky, Anatoly Alexandrovich ORCID: https://orcid.org/0000-0003-0630-8279 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

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Abstract

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) Mathematics
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
URI:	https://orca.cardiff.ac.uk/id/eprint/22986

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