Detection Of Outliers And Structural Breaks In Structural Time Series Model Using Indicator Saturation Approach - Rose, Farid Zamani Che (2023)
The presence of structural changes, specifically outliers and structural breaks, adversely affects the estimation of economic and financial indicators in terms of the model accuracy and forecasting performance. Focusing on the detection of outliers and structural breaks, which has recently gained growing research interest, this study aimed to examine the performance of indicator saturation, as an extension of the general-to-specific (GETS) modelling, in detecting these structural changes in structural time series model framework. The proposed technique is capable to detect the location, duration, magnitude and number of structural changes in time series data. To date, prior studies only considered using Autometrics embodied in OxMetrics to apply this approach in static data generating process (DGP). Addressing this gap, this study used the gets package in R to examine the performance of indicator saturation in dynamic model viz state space model. Through Monte Carlo simulations, the performance of indicator saturation was evaluated in terms of potency and gauge. Based on the simulation results, the sequential selection algorithm outperformed the non-sequential selection approach in the automatic GETS model selection procedure. The results also suggested α = 1/T as the optimum level of significance level.