The Application of the Structure Time Series Model on Seasonal Adjustment——Compared with X-12 Seasonal Adjustment Method
结构时间序列模型在季节调整方面的应用——与X-12季节调整方法的比较分析

In the paper,we construct a new seasonal adjustment method of time series on the basis of the structural time series model.By researching the structure of economic time series using ARIMA model,we firstly establish the expression of trend-cycle component according to the order of integration(d),and set up different forms of structural time series models.In the structure time series model,the economic indicator is decomposed into trend,cycle,seasonal and irregular components,which are unobserved and thus can't be estimated by classical regression way.So we estimate the model in the form of state space.Further,we use the model to decompose China's economic time series,such as GDP,Total Retail Sales of Consumer Goods,etc.Moreover we compare our model's results with X-12 seasonal adjustment method's,and the empirical conclusions show that the structure time series model is more stable when it is used to decompose seasonal component.