我国GDP增长率序列中趋势成分和周期成分的分解
发布时间：2019-03-15 12:59
【摘要】：本文使用H P滤波、时间趋势平稳、ARMA趋势平稳和状态空间分解等趋势分解方法 ,对我国GDP增长率序列进行了趋势分解 ,并对各种周期成分进行了对比检验。我们发现 ,这些分解方法得到的周期成分具有类似的统计性质 ,但就残差序列的白噪声检验来说 ,双变量状态空间模型的分解效果最为显著 ,因此应该采用状态空间模型进一步分析我国的经济周期性质
[Abstract]:In this paper, the trend decomposition methods such as H-P filter, stationary time trend, stationary ARMA trend and state space decomposition are used to decompose the growth rate series of GDP in China, and a comparative test of various periodic components is carried out. We find that the periodic components obtained by these decomposition methods have similar statistical properties, but as far as white noise test of residual sequence is concerned, the decomposition effect of the bivariate state space model is the most significant. Therefore, the state space model should be used to further analyze the economic cycle properties of our country.
【作者单位】： 吉林大学数量经济研究中心 吉林大学数量经济研究中心
【基金】：国家社会科学基金项目 ( 0 2BJY0 1 9) 教育部重大项目 ( 0 2JAZJD790 0 7)资助
【分类号】：F222
本文编号：2440644
[Abstract]:In this paper, the trend decomposition methods such as H-P filter, stationary time trend, stationary ARMA trend and state space decomposition are used to decompose the growth rate series of GDP in China, and a comparative test of various periodic components is carried out. We find that the periodic components obtained by these decomposition methods have similar statistical properties, but as far as white noise test of residual sequence is concerned, the decomposition effect of the bivariate state space model is the most significant. Therefore, the state space model should be used to further analyze the economic cycle properties of our country.
【作者单位】： 吉林大学数量经济研究中心 吉林大学数量经济研究中心
【基金】：国家社会科学基金项目 ( 0 2BJY0 1 9) 教育部重大项目 ( 0 2JAZJD790 0 7)资助
【分类号】：F222
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