中国核心CPI随机游走识别和异常点分析  

Identification of Core CPI with Outlier Random Walk in China

作　　者：顾光同[1,2] 许冰[1] Gu Guangtong Xu Bing(Research Institute of Economic Statistics and Quantitative Economics, Zhejiang Gongshang University School of Science, Zhejiang A ＆ F University)

机构地区：[1]浙江工商大学经济统计与数量经济研究所 [2]浙江农林大学理学院

出　　处：《数量经济技术经济研究》2016年第12期144-158,共15页Journal of Quantitative & Technological Economics

基　　金：国家自然科学基金(71403247);全国统计科学研究计划项目(2013LY123;2013LZ15;2015LZ53);浙江省哲学社会科学基金(14NDJC143YB);浙江省高校人文社科重点基地(统计学;应用经济学);浙江省教育厅项目(Y201223259);浙江省国内访学项目(FX2014035)的资助

摘　　要：核心CPI能更真实地反映宏观经济运行趋势。然而,如何测算核心CPI一直备受关注。本文构建了一组带有异常因子的随机游走模型,利用MCMC法和Gibbs抽样,对时变参数进行估计,不仅从CPI中分离出核心CPI,而且对非核心CPI,通过捕捉到的异常点,发现与中国政策效应相吻合。本文仅利用了中国单一的CPI数据,不需要CPI子类权重测算及其再分配。进一步,通过与Wind核心CPI比较,以及Marques等(2003)核心CPI评价方法的检验,发现本文的核心CPI更具合理性和科学性。Core CPI can more realistically reflect the macroeconomic trends. However, how to measure core CPI has been a matter of great concern. This paper constructs a set of random walk model with abnormal factors and estimates time varying parameters by using the MCMC method and the Gibbs sampling in the models. The results show that not only core CPI is isolated from CPI, but also the abnormal points of non-core CPI are captured, which are consistent with the policy effects in China This random walk identification method uses China single CPI data without calcu- lating CPI sub-class weights and their redistribution. Further, compared with Wind core CPI and tested with the core CPI evaluation method from Marques and Neves （2003）, the core CPI proposed in this paper is more reasonable and scientific.

关 键 词：通货膨胀 核心CPI 随机游走识别 异常点分析 

分 类 号：F224.0[经济管理—国民经济] C813[社会学—统计学]