摘要：Zhang T., Tang K., Liu T.X., Jiang T.F.* High Frequency Online Inflation and Term Structure of Interest Rates: Evidence from China

论文基本信息：

Zhang T., Tang K., Liu T.X., Jiang T.F.* High Frequency Online Inflation and Term Structure of Interest Rates: Evidence from China[J]. Journal of Empirical Finance, 2025(83), 101626.

论文摘要：

In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation significantly predicts the yield curve, especially the slope factor, whereas the monthly official inflation cannot predict the yield curve and is instead predicted by the yield curve factors. The mechanism analysis reveals that, due to low price stickiness, online inflation is more sensitive to short-term economic fluctuations and better reflects money market liquidity, thereby having significant predictive power for short-term interest rates and the slope factor. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.

iCPI项目组简介：

清华大学iCPI项目组成立于2015年9月，成立至今近10年。iCPI具有高频实时发布、计算机算法自动运行和大数据价格来源等显著优势特征，具体指数实时发布于网站http://www.bdecon.com, 并于2017年6月在国际著名金融信息服务商Bloomberg上线、2019年8月在国内重要金融信息服务商Wind上线。

关于iCPI的设计，可参见论文：

刘涛雄, 汤珂, 姜婷凤, 仉力. 一种基于在线大数据的高频CPI指数的设计及应用[J].数量经济技术经济研究, 2019,(9):81-101

来源：大数据经济观察