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A Bayesian Sequential Method for Identifying Structural Shocks and Adaptive Forecasting in State Space Model

The repeated occurrences of interventions in observations make most forecasting models fail to produce appropriate forecasts. The purpose of this study is to propose the adaptive forecasting procedure based on sequential identifications of interventions and adjusting forecast to them in general linear state space model. For the detection of an intervention, the general statistic covering the various types of interventions are derived in the form of posterior odds for non-intervened versus intervened model. And assuming known as the occurrence of the intervention during a given period, the joint posterior distribution of intervening point and type is also derived for identification. The proposed procedure is tested by simulation and empirical study based on the innovational state space model of additive and non-seasonal exponential smoothing.