Predicting China's CPI by Scanner Big Data

TL;DR

This paper constructs a food CPI index using scanner big data from supermarkets in China, verifies its reliability, and employs machine learning models to predict CPI growth, offering a new approach for economic monitoring and inflation prediction.

Contribution

It introduces the S-FCPI index based on scanner data and demonstrates its effectiveness in predicting China's CPI growth using advanced machine learning models.

Findings

S-FCPI correlates well with China's official CPI.

Machine learning models outperform traditional time series models in CPI prediction.

Scanner data provides high-frequency, geographically wide coverage for economic analysis.

Abstract

Scanner big data has potential to construct Consumer Price Index (CPI). This work utilizes the scanner data of supermarket retail sales, which are provided by China Ant Business Alliance (CAA), to construct the Scanner-data Food Consumer Price Index (S-FCPI) in China, and the index reliability is verified by other macro indicators, especially by China's CPI. And not only that, we build multiple machine learning models based on S-FCPI to quantitatively predict the CPI growth rate in months, and qualitatively predict those directions and levels. The prediction models achieve much better performance than the traditional time series models in existing research. This work paves the way to construct and predict price indexes through using scanner big data in China. S-FCPI can not only reflect the changes of goods prices in higher frequency and wider geographic dimension than CPI, but also provide a new perspective for monitoring macroeconomic operation, predicting inflation and understanding other economic issues, which is beneficial supplement to China's CPI.