CPI之GEKS指数序列更新方法及窗口长度选择问题辨析

大数据来源下CPI指数的创新编制,对及时了解新经济时代的物价走向和识别通胀危机、预测宏观经济拐点以实现我国通胀治理现代化、推动经济平稳和高质量发展具有重大意义。GEKS多边指数是近些年国际学术界重点研发的大数据热点价格指数,但其构造方法颇具争议。借助超市扫描大数据,就GEKS指数序列更新方法、窗口长度选择等学界难题开展理论与实证研究,获得了以下富有启发性的结论:①GEKS指数序列更新方法2、3应用效果相对较差;②随着窗口长度的增加,GEKS环比价格指数会趋于单位值,不同更新方法下的GEKS链式指数也会呈现一定的趋同性;而GEKS指数的通胀趋势判断力却不受此影响,但更新方法的选择却会导致其不同的通胀趋势预测结果;③更新方法4会随着窗口长度的增加而呈现更强的替代偏误,方法1却没有出现明显的替代偏误。综合而言,更新方法1和13个月窗口长度应该是编制GEKS指数序列更为合理的组合方式。

Analysis on CPI’s GEKS Index Sequence Update Method and Selection of Window Length

The innovative compilation of CPI index based on big data is of great significance for timely understanding the price trend in the new economic era, identifying the inflation crisis, predicting the economic turning point, realizing the modernization of China’s inflation governance and promoting the stable high-quality development of the economy. The GEKS Multilateral Index is a big data-related hotspot price index researched and developed in the international academic community in recent years, but its construction method is quite controversial. Based on the big data of supermarket scanning, in connection with the unresolved problems such as its update method and window length selection, we draw some enlightening conclusions from the theoretical and empirical research: 1) The update methods 2 and 3 have relatively poor application effects. 2) With the increase of the window length, the GEKS sequential price index tends to the unit value, and the GEKS chain indexes under different update methods also show certain convergence. However, the GEKS index’s inflation trend judgment is not affected by the window length, but the choice of update method can lead to different inflation trend prediction results. 3) Update method 4 will present stronger substitution bias with the increase of window length, while there is no obvious substitution bias in method 1. In general, it may be a more reasonable combination for update method 1 and 13 months’ window length to compile GEKS index sequence.