Research on the stability and robustness of seasonal adjustment procedure

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General state-space models are applied to various types of problems in seasonal adj ustment. Especially, Monte Carlo filter, smoothing and self-organizing state-space model are found to be useful for stable and robust treatment of statistical seasonal adjustment. To put it precisely, new methods are developed for multiplicative type non-linear seasonal adjustment, for seasonal adjustment in small count data, and for automatic outlier detection in seasonal adjustment. Furthermore, model averaging type seasonal adjustment has been explored. In other words, we do not confine the seasonal model to a specific one but consider and monitor all the possible seasonal models, and realize prediction by weighting these models. On the other hand, as a generalization of time serics problem, removing intraday periodicity in high frequent financial data is considered via point process modeling by conditional intensity approach. It is shown that commonly employed method that use spline smoothing to estimate time-of-day function does not completely remove such intraday periodicity. In the final year of this research grant, an intemational symposium on statistical seasonal adjustment was held in Tokyo under the title 'Modeling Seasonality and Periodicity' on January 3 1 and February I, which ended in a great success. Newly developed software E-Decomp was released and distributed to the conference participants on free CD-ROM.