基于季节调整春节模型的CPI建模预测
发布时间:2018-10-23 07:54
【摘要】:如何衡量并消除以春节为代表的移动节假日影响是我国季节调整中的一项重点和难点。文章介绍了三区段变权重春节模型,选取由2001年1月至2013年6月CPI环比数据转换的物价指数,从数据中分离出趋势成分、季节因子、春节因子和随机因子,进行春节影响调整,再对调整后时间序列分别采用传统的指数平滑法和X-12-ARIMA方法进行2013年7月至2014年2月的短期建模预测。结果表明选取的CPI指数序列确实受春节因素的影响,经过春节假日调整后的温特指数模型和X-12-ARIMA模型都预测出了比较准确的结果。
[Abstract]:How to measure and eliminate the influence of the moving holidays represented by the Spring Festival is an important and difficult point in the seasonal adjustment of our country. This paper introduces a three-section variable weight Spring Festival model, selects the price index of CPI ring data conversion from January 2001 to June 2013, separates the trend component, seasonal factor, Spring Festival factor and random factor from the data, and adjusts the influence of Spring Festival. Then the modified time series are predicted by using the traditional exponential smoothing method and X-12-ARIMA method respectively from July 2013 to February 2014. The results show that the selected CPI exponent sequence is really influenced by the Spring Festival factors. Both the Winter index model and the X-12-ARIMA model after the Spring Festival holiday adjustment predict more accurate results.
【作者单位】: 北京科技大学数理学院;
【分类号】:F726;F224
[Abstract]:How to measure and eliminate the influence of the moving holidays represented by the Spring Festival is an important and difficult point in the seasonal adjustment of our country. This paper introduces a three-section variable weight Spring Festival model, selects the price index of CPI ring data conversion from January 2001 to June 2013, separates the trend component, seasonal factor, Spring Festival factor and random factor from the data, and adjusts the influence of Spring Festival. Then the modified time series are predicted by using the traditional exponential smoothing method and X-12-ARIMA method respectively from July 2013 to February 2014. The results show that the selected CPI exponent sequence is really influenced by the Spring Festival factors. Both the Winter index model and the X-12-ARIMA model after the Spring Festival holiday adjustment predict more accurate results.
【作者单位】: 北京科技大学数理学院;
【分类号】:F726;F224
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