关键词: 连续性抽样调查, 平衡轮换模式, 时间序列数据, 季节调整, 抽样误差

Abstract:

Traditional seasonal adjustment methods are focusing on building the model and estimation for time series economic sector, assuming that error is a white noise, ignoring the correlativity of the error’s structure. In order to develop seasonal adjustment accuracy, we study the influence of sampling error to seasonal changes based on the balanced rotation sample survey, processed from successive sampling survey. Then, this paper builds the general seasonal adjustment model, using Kalman filtering to estimate the parameters, and evaluates the model accuracy using forecast error and error variance. At last, taking 12～0 rotation scheme of China’s urban household survey as an example, we make the empirical analysis of the seasonal adjustment model including sampling error in order to verify the effectiveness of the method.

Key words: Successive Sampling Survey, Balanced Rotation Scheme, Time Series Data, Seasonal Adjustment, Sampling Error