A Study Of The Theory And Application Of Structural Time Series Model In Seasonal Adjustment

Seasonal adjustment on economic series has always been one of the focuses in statistics and econometrics in many countries. In order to inspect the variety law and explain the economic significance of the data effectively, the seasonal adjustment must get the trend and season signals from the real economic series actually and entirely. Since Macauley advanced the ratio-to-moving average method for seasonal adjustment in1931, the nonparametric method based on moving average filters and the parametric method based on classic time series model signal extraction both have been improved well. However, as more and more problems have been raised in seasonal adjustment, the inherent default in fundamentals brings these two methods more and more weak in seasonal adjustment. Then, the structural time series models based on state space method have been introduced into the seasonal adjustment research. As the structural time series models are more flexible, more and more problems during seasonal adjustment can be fitted and solved. So the structural time series models have been new development direction of the theory and method of seasonal adjustment. This paper improved the HS seasonal model purposively, which is the classic one of the structural time series models, to give the HS-SH model and HS-ST model to fit seasonal heteroscedasticity and seasonal trend, and gave the ways to test the existence of the seasonal heteroscedasticity and the seasonal trend through these two models to make the structural time series model method more complete.At first, this paper contrasted the X11and SEATS seasonal adjustment methods completely to summarize the merits and faults of them. Then, it presented the basic structure and estimation method of the structural time series models in detail. And then, it contrasted the theory fundamentals and basic structure of the structural time series models, which can be used in seasonal adjustment, to summarize the merits and faults of each one. And then, it advanced the improved HS model, which can fit the seasonal heteroscedasticity and seasonal trend, and presented the LR test on seasonal heteroscedasticity and the AIC test on seasonal trend based on the improved HS model. At last, this paper used the improved HS model to do seasonal adjustment on China’s monthly tax revenues series and China’s monthly electricity generation series to gain their features of trend and seasonal move, and at the same time, it contrasted the result of X11and SEATS method on these two series to find that the improved HS model method more effective.In the theory research aspect, this paper’s main innovation are as follows:First, this paper contrasted almost every structural model’s fundamentals and basic structure, likes DS, TS, HS and SSLLT model, which can be used for seasonal adjustment, to summarize the merits and faults of each one. Based on this, the improved HS model, which can solve actual problems such as seasonal heteroscedasticity and seasonal trend, and its estimation, outlier revision and forecast have been advanced.Second, for the improved HS model, this paper gave methods for testing the existence of seasonal heteroscedasticity and seasonal trend. Moreover, it gave the distributions of the LR statistics for the test of seasonal heteroscedasticity, found all influencing factors of the distribution, and summarized the size and power of the test under many conditions with different values of the factors.Third, this paper designed the programming module for the estimation and test of the improved HS model based on the MATLAB program package for state space model to make the improved HS model’s estimation and test achievable.In the empirical research aspect, this paper’s main innovation are as follows:First, this paper used the HS-SH model to do seasonal adjustment on China’s monthly tax revenues series during1991January to2011December. The result indicated that China’s tax revenues has a steady increase trend, whose increase rate is higher than the GDP’s, and can be impacted by external crisis easily. Moreover, China’s tax revenues have an obvious seasonal feature, which is accompanied with variety and heteroscedasticity because of the reform of the taxation system.Second, this paper used the combined model, which is combined by the HS-SH model and the HS-ST model, to do seasonal adjustment on China’s monthly electricity generation series during1991January to2011December. The result indicated that China’s electricity generation has a relative steady increase trend, which is affected by the influence of China’s macroeconomic endogenous increase to heavy industry. Like the tax revenues, China’s electricity generation can be impacted by external crisis easily, too. Moreover, China’s electricity generation have an obvious seasonal feature, which is accompanied with trend and heteroscedasticity because of the spring festival holiday, global warming, and home electricity increasing.