Article: Structural breaks detection using step-indicator saturation technique in state-space model Journal: International Journal of Computational Economics and Econometrics (IJCEE) 2024 Vol.14 No.1 pp.61 - 80 Abstract: Recently, there has been a lot of interest in identifying structural breaks in economic time series. Failing to capture any structural breaks may have a pernicious effect on model estimation due to significant forecast errors after such breaks and inappropriate tests. Therefore, this study proposed a step-indicator saturation (SIS) technique as an extension of the general-to-specific (GETS) modelling framework for detecting any structural changes in time series. Monte Carlo simulations assessed the performance of the SIS in the local level model based on potency and gauge metrics using the 'gets' package in the R programming language. Sequential selection outperformed the non-sequential approach in the automatic GETS model selection procedure. Accordingly, this study applied the SIS technique to the Financial Times Stock Exchange (FTSE) Bursa Malaysia Hijrah Shariah and FTSE USA Shariah using a split-half approach and sequential selection. The retained indicators in the terminal model were selected based on the sequential and non-sequential algorithms. It was found that the retained indicators in both indices collided with the financial crises in 2008-2009. Overall, the proposed technique offers an effective approach to detect unknown locations, magnitudes, and structural break signs in a structural times series framework. Inderscience Publishers - linking academia, business and industry through research

Title: Structural breaks detection using step-indicator saturation technique in state-space model

Authors: Farid Zamani Che Rose; Mohd Tahir Ismail; Nur Aqilah Khadijah Rosili

Addresses: Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia ' School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Penang, Malaysia ' Faculty of Computing and Engineering, Quest International University, 30250 Ipoh, Perak, Malaysia

Abstract: Recently, there has been a lot of interest in identifying structural breaks in economic time series. Failing to capture any structural breaks may have a pernicious effect on model estimation due to significant forecast errors after such breaks and inappropriate tests. Therefore, this study proposed a step-indicator saturation (SIS) technique as an extension of the general-to-specific (GETS) modelling framework for detecting any structural changes in time series. Monte Carlo simulations assessed the performance of the SIS in the local level model based on potency and gauge metrics using the 'gets' package in the R programming language. Sequential selection outperformed the non-sequential approach in the automatic GETS model selection procedure. Accordingly, this study applied the SIS technique to the Financial Times Stock Exchange (FTSE) Bursa Malaysia Hijrah Shariah and FTSE USA Shariah using a split-half approach and sequential selection. The retained indicators in the terminal model were selected based on the sequential and non-sequential algorithms. It was found that the retained indicators in both indices collided with the financial crises in 2008-2009. Overall, the proposed technique offers an effective approach to detect unknown locations, magnitudes, and structural break signs in a structural times series framework.

Keywords: structural breaks; step-indicator saturation; SIS; Monte Carlo; model selection; state-space model; general-to-specific; GETS.

DOI: 10.1504/IJCEE.2024.135656

International Journal of Computational Economics and Econometrics, 2024 Vol.14 No.1, pp.61 - 80

Accepted: 21 Jun 2023
Published online: 20 Dec 2023 *

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Title: Structural breaks detection using step-indicator saturation technique in state-space model

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