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

Modelling European industrial production with multivariate singular spectrum analysis: a cross industry analysis

Silva, Emmanuel Sirimal, Hassani, Hossein and Heravi, Saeed 2018. Modelling European industrial production with multivariate singular spectrum analysis: a cross industry analysis. Journal of Forecasting 37 (3) , pp. 371-384. 10.1002/for.2508
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 22 January 2020 due to copyright restrictions.

Download (226kB)

Abstract

In this paper, an optimized multivariate singular spectrum analysis (MSSA) approach is proposed to find leading indicators of cross‐industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French, and UK economies. Both recurrent and vector forecasting algorithms of horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate singular spectrum analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data are rigorously tested for normality, seasonal unit root hypothesis, and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that, for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r, which minimizes a loss function.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Wiley Blackwell
ISSN: 0277-6693
Date of First Compliant Deposit: 18 December 2017
Date of Acceptance: 2 December 2017
Last Modified: 29 Jan 2019 13:58
URI: http://orca.cf.ac.uk/id/eprint/107629

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics