Detection procedure of structural changes in state-space models: impulse and steps indicator saturation technique - Farid Zamani Che Rose,; Mohd Tahir Ismail,; Muhammad Aslam Safari,; Nur Aqilah Khadijah Rosili,; Muhammad Fadhil Marsani, (Penerbit Universiti Kebangsaan Malaysia, 2025)
The presence of extreme structural change in a sequence of data points over time may have a detrimental impact on the estimation of economic and financial indicators. Anomalies caused by these extreme values can distort the estimated parameters, diminish the accuracy of the time series model, and potentially lead to inaccurate forecasts. In this research, a general-to-specific modeling approach is utilized to identify the structural changes through indicator saturation within the framework of a state-space models as an alternative to current method. By focusing on impulse and steps indicator saturation, this study evaluates their effectiveness through Monte Carlo simulations that are replicated 1000 times. The Monte Carlo experiments demonstrate that the efficiency of indicator saturation is heavily dependent on factors such as the magnitude of the structural change, the level of statistical significance, and the position of an extreme value within the series. Subsequently, this study employs the combined impulse and steps indicator saturation to detect structural breaks in the FTSE 100 daily closed stock price index. The most important findings relate to the coefficients for the structural breaks at and are estimated at and , respectively. The findings show that the characteristics, position, and direction of the extreme values detected by impulse indicator saturation coincide with the occurrence of the COVID-19 pandemic, which has had a global impact on economic activities. This finding may lead to better understanding of how the stock markets in UK reacts to government policy due to the COVID-19 pandemic.