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Modeling European industrial production with multivariate singular spectrum analysis: A cross†industry analysis

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  • Emmanuel Sirimal Silva
  • Hossein Hassani
  • Saeed Heravi

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.

Suggested Citation

  • Emmanuel Sirimal Silva & Hossein Hassani & Saeed Heravi, 2018. "Modeling European industrial production with multivariate singular spectrum analysis: A cross†industry analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 371-384, April.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:3:p:371-384
    DOI: 10.1002/for.2508
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    Citations

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    Cited by:

    1. Hossein Hassani & Mohammad Reza Yeganegi & Emmanuel Sirimal Silva, 2018. "A New Signal Processing Approach for Discrimination of EEG Recordings," Stats, MDPI, vol. 1(1), pages 1-14, November.
    2. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    3. Hatice Oncel Cekim & Coşkun Okan Güney & Özdemir Şentürk & Gamze Özel & Kürşad Özkan, 2021. "A novel approach for predicting burned forest area," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 2187-2201, January.
    4. Hatice Öncel Çekim & Ahmet Koyuncu, 2022. "The Impact of Google Trends on the Tourist Arrivals: A Case of Antalya Tourism," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(1), pages 1-14, June.
    5. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).
    6. Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed & Huang, Xu, 2019. "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, Elsevier, vol. 74(C), pages 134-154.
    7. Yoga Sasmita & Heri Kuswanto & Dedy Dwi Prastyo, 2024. "State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export," Forecasting, MDPI, vol. 6(1), pages 1-18, February.
    8. Mahdi Kalantari & Hossein Hassani, 2019. "Automatic Grouping in Singular Spectrum Analysis," Forecasting, MDPI, vol. 1(1), pages 1-16, October.
    9. Hassani, Hossein & Silva, Emmanuel Sirimal, 2018. "Forecasting UK consumer price inflation using inflation forecasts," Research in Economics, Elsevier, vol. 72(3), pages 367-378.
    10. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    11. Juan B'ogalo & Pilar Poncela & Eva Senra, 2020. "Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis," Papers 2007.07561, arXiv.org, revised Aug 2023.

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