基于带跳时变系数模型的PPI与CPI相关性研究
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摘要
2008年以来,我国PPI与CPI走势出现了多次背离与分化,从整体上看,两者相关性很弱。但从动态视角来看,由于相关关系可能会因时而变,整体相关性有可能被关系本身的方向和强弱变化所削弱甚至掩盖。为准确反映两者相关性的动态变化,本文放宽时变系数函数的光滑性假设,提出了带跳时变系数模型,并给出一种非参数三步估计方法:①估计系数函数中跳点的位置和个数;②基于估计的跳点和Bootstrap方法选择的窗宽给出系数函数的最终估计;③利用蒙特卡洛模拟评价本文提出的非参数估计和窗宽选择方法的有限样本性质。通过对2008年1月至2017年12月我国PPI和CPI月度同比数据的实证分析,本文发现该模型能较好地刻画PPI与CPI相关性的时变和带跳特征,验证了该模型的应用价值。
Since 2008, the trend of PPI and CPI in China has deviated and split up many times. On the whole, both correlate weakly. However, from a dynamic perspective, because the correlation may change over time, the overall correlation may be weakened or even concealed by the veer and extent of correlations itself. In order to reflect the dynamic changes of the PPI and CPI correlations, this paper propose a time-varying coefficient models with jumps and offers a nonparametric three-step estimation procedure in a hypothesis of smoothness in the eased time-varying coefficient function. ① the position and number of jumps are estimated in the coefficient function; ② the final estimation of the coefficient function is given based on the estimated jumps and the bandwidth chosen by the bootstrap method; ③ some Monte Carlo simulations are used to evaluate the finite sample performance of the proposed nonparametric estimation and bootstrap method. By empirically studying the PPI and CPI data in China from January 2008 to December 2017, it is found that the model can well depict the time-varying and band jumping characteristics of PPI and CPI, and furthermore justify the value in applying this model.
Since 2008, the trend of PPI and CPI in China has deviated and split up many times. On the whole, both correlate weakly. However, from a dynamic perspective, because the correlation may change over time, the overall correlation may be weakened or even concealed by the veer and extent of correlations itself. In order to reflect the dynamic changes of the PPI and CPI correlations, this paper propose a time-varying coefficient models with jumps and offers a nonparametric three-step estimation procedure in a hypothesis of smoothness in the eased time-varying coefficient function. ① the position and number of jumps are estimated in the coefficient function; ② the final estimation of the coefficient function is given based on the estimated jumps and the bandwidth chosen by the bootstrap method; ③ some Monte Carlo simulations are used to evaluate the finite sample performance of the proposed nonparametric estimation and bootstrap method. By empirically studying the PPI and CPI data in China from January 2008 to December 2017, it is found that the model can well depict the time-varying and band jumping characteristics of PPI and CPI, and furthermore justify the value in applying this model.
引文
[1]孙坚强, 崔小梅, 蔡玉梅. PPI和CPI的非线性传导: 产业链与价格预期机制[J].经济研究, 2016(10):54-68.
[2]刘敏, 张燕丽, 杨延斌.PPI与CPI关系探析[J].统计研究,2005(2):24-27.
[3]徐伟康. 对《消费者价格指数与生产者价格指数: 谁带动谁?》一文的质疑[J].经济研究, 2010(5):139-148+158.
[4]张成思. 长期均衡、价格倒逼与货币驱动——我国上中下游价格传导机制研究[J].经济研究, 2010(6):42-52.
[5]贺力平, 樊刚, 胡嘉妮. 消费者价格指数与生产者价格指数: 谁带动谁? [J].经济研究,2008(11):16-26.
[6]贺力平, 樊纲, 胡嘉妮. 消费者价格指数与生产者价格指数:对徐伟康商榷文章的回复意见[J].经济研究, 2010(5):149-154.
[7]刘凤良, 鲁旭. CPI与PPI的“虚假传导”及其修正——一个相对稳健的实证框架[J].数量经济技术经济研究, 2011(8):91-102,114.
[8]Nishiyama Y, et al. A Consistent Nonparametric Test for Nonlinear Causality—Specification in Time Series Regression [J].Journal of Econometrics, 2011(1):112-127.
[9]杨子晖, 赵永亮, 柳建华. CPI 与PPI 传导机制的非线性研究: 正向传导还是反向倒逼[J].经济研究,2013(3):83-95.
[10]Diks C, Panchenko V.A New Statistic and Practical Guidelines for Nonparametric Granger Causality Testing[J].Journal of Economic Dynamics and Control,2006(9):1647-1669.
[11]Robinson P M. Root-n-consistent Semiparametric Regression [J].Econometrica, 1988(4):931-954.
[12]Orbe S, Ferreira E, Rodriguez-Poo J. Nonparametric Estimation of Time Varying Parameters under Shape Restrictions [J].Journal of Econometrics, 2005(1):53-77.
[13]Cai Z. Trending Time-varying Coefficient Time Series Models with Serially Correlated Error[J]. Journal of Econometrics, 2007(1):163-188.
[14]Li Q, Ouyang D, Racine J S. Categorical Semiparametric Varying-coefficient Models [J].Journal of Applied Econometrics, 2013(4):551-579.
[15]Su C W, et al. Is There any Relationship between Producer Price Index and Consumer Price Index in Slovakia? A Bootstrap Rolling Approach [J].Ekonomick■ ■asopis, 2016(7):611-628.
[16]Fan J, Zhang W. Statistical Estimation in Varying Coefficient Models [J].Annals of Statistics, 1999(5):1491-1518.
(1)2016年,国家统计局按照统计制度要求,对CPI调查目录进行了调整,2016年1月开始使用2015年作为新一轮的对比基期。国家数据库将2015年之前和2016之后CPI数据分别列示。新基期调查目录和规格品与国际标准更为接近,一些新产品新服务纳入其中,能进一步反映居民消费和经济结构的变化,但对数据的纵向对比分析带来不便。通过分析国家统计局目录调整的几个主要变化,本文认为,本轮基期的调整对分类消费价格指数影响较大,在CPI整体层面上看,影响则并不明显。因此本文直接将2016年前后月份CPI数据拼接。
[2]刘敏, 张燕丽, 杨延斌.PPI与CPI关系探析[J].统计研究,2005(2):24-27.
[3]徐伟康. 对《消费者价格指数与生产者价格指数: 谁带动谁?》一文的质疑[J].经济研究, 2010(5):139-148+158.
[4]张成思. 长期均衡、价格倒逼与货币驱动——我国上中下游价格传导机制研究[J].经济研究, 2010(6):42-52.
[5]贺力平, 樊刚, 胡嘉妮. 消费者价格指数与生产者价格指数: 谁带动谁? [J].经济研究,2008(11):16-26.
[6]贺力平, 樊纲, 胡嘉妮. 消费者价格指数与生产者价格指数:对徐伟康商榷文章的回复意见[J].经济研究, 2010(5):149-154.
[7]刘凤良, 鲁旭. CPI与PPI的“虚假传导”及其修正——一个相对稳健的实证框架[J].数量经济技术经济研究, 2011(8):91-102,114.
[8]Nishiyama Y, et al. A Consistent Nonparametric Test for Nonlinear Causality—Specification in Time Series Regression [J].Journal of Econometrics, 2011(1):112-127.
[9]杨子晖, 赵永亮, 柳建华. CPI 与PPI 传导机制的非线性研究: 正向传导还是反向倒逼[J].经济研究,2013(3):83-95.
[10]Diks C, Panchenko V.A New Statistic and Practical Guidelines for Nonparametric Granger Causality Testing[J].Journal of Economic Dynamics and Control,2006(9):1647-1669.
[11]Robinson P M. Root-n-consistent Semiparametric Regression [J].Econometrica, 1988(4):931-954.
[12]Orbe S, Ferreira E, Rodriguez-Poo J. Nonparametric Estimation of Time Varying Parameters under Shape Restrictions [J].Journal of Econometrics, 2005(1):53-77.
[13]Cai Z. Trending Time-varying Coefficient Time Series Models with Serially Correlated Error[J]. Journal of Econometrics, 2007(1):163-188.
[14]Li Q, Ouyang D, Racine J S. Categorical Semiparametric Varying-coefficient Models [J].Journal of Applied Econometrics, 2013(4):551-579.
[15]Su C W, et al. Is There any Relationship between Producer Price Index and Consumer Price Index in Slovakia? A Bootstrap Rolling Approach [J].Ekonomick■ ■asopis, 2016(7):611-628.
[16]Fan J, Zhang W. Statistical Estimation in Varying Coefficient Models [J].Annals of Statistics, 1999(5):1491-1518.
(1)2016年,国家统计局按照统计制度要求,对CPI调查目录进行了调整,2016年1月开始使用2015年作为新一轮的对比基期。国家数据库将2015年之前和2016之后CPI数据分别列示。新基期调查目录和规格品与国际标准更为接近,一些新产品新服务纳入其中,能进一步反映居民消费和经济结构的变化,但对数据的纵向对比分析带来不便。通过分析国家统计局目录调整的几个主要变化,本文认为,本轮基期的调整对分类消费价格指数影响较大,在CPI整体层面上看,影响则并不明显。因此本文直接将2016年前后月份CPI数据拼接。