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近年我国消费率持续下降,在我国消费社会处于转型升级的重要时期,投资与消费的失衡是当前宏观经济中最严重的问题之一。本文在基于传统的Kalman滤波状态空间季节调整模型的基础上,用均方根信息滤波的方法对状态空间季节调整模型进行改进,充分利用R软件中DECOMP程序包的优势将2000年1月至2010年12月我国居民消费非平稳序列进行分解。所得结论:(1)二阶AR成分模型最优。交易日对我国消费品零售额影响不显著。(2)我国社会消费品零售额有两阶段指数发展趋势特征,2003年是趋势变化转折点,后期比前期增长快。具有中间低、两头高的U型季节特征。(3)得到非常平滑的月环比增长率,为经济监测提供了更稳定的依据。
In recent years, the consumption rate in our country has continued to decline. In the important period of transformation and upgrading of the consumer society in our country, the imbalance between investment and consumption is one of the most serious problems in the present macroeconomy. Based on the traditional Kalman filtering state-space seasonal adjustment model, this paper improves the state-space seasonal adjustment model by using the root-mean-square information filtering method. Taking full advantage of the advantages of the DECOMP package in R software, from January 2000 to 2010 In December, China’s resident consumption non-stationary sequence decomposition. The conclusions obtained: (1) The second-order AR component model is optimal. Trading day on the retail sales of consumer goods in China is not significant. (2) The retail sales of social consumer goods in China have the characteristics of a two-stage index trend of development. In 2003, the turning point was the turning point of the trend. The retail sales of social consumer goods increased rapidly in the later period than in the previous period. With the middle of the low, two high U-season seasonal features. (3) A very smooth monthly growth rate has been achieved, providing a more stable basis for economic monitoring.