论文标题
扫描仪大数据预测中国的CPI
Predicting China's CPI by Scanner Big Data
论文作者
论文摘要
Scanner Big Data具有构建消费者价格指数（CPI）的潜力。这项工作利用由中国蚂蚁商业联盟（CAA）提供的超市零售的扫描仪数据来构建中国的Scanner-DATA食品消费价格指数（S-FCPI），并且该指数可靠性由其他宏观指标验证，尤其是由中国CPI验证。不仅如此，我们还基于S-FCPI构建了多个机器学习模型，以定量预测几个月中的CPI增长率，并在定性上预测这些方向和水平。在现有研究中，预测模型的性能要比传统的时间序列模型要好得多。这项工作铺平了通过在中国使用扫描仪的大数据来构建和预测价格指数的方式。 S-FCPI不仅可以反映出比CPI更高频率和更广泛的地理位置的商品价格变化，而且还为监测宏观经济运营提供了新的观点，预测通货膨胀并理解其他经济问题，这对中国CPI是有益的补充。
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.
