Forecasting UK industrial production with multivariate singular spectrum analysis

Hassani, Hossein, Heravi, Saeed ORCID: https://orcid.org/0000-0002-0198-764X and Zhigljavsky, Anatoly Alexandrovich ORCID: https://orcid.org/0000-0003-0630-8279 2013. Forecasting UK industrial production with multivariate singular spectrum analysis. Journal of Forecasting 32 (5) , pp. 395-408. 10.1002/for.2244

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Abstract

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models. We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	singular spectrum analysis; forecasting; causality; industrial production series
Publisher:	Wiley
ISSN:	1099-131X
Last Modified:	19 Oct 2022 10:44
URI:	https://orca.cardiff.ac.uk/id/eprint/25346

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