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居民网络消费价格指数编制:实践经验、问题与展望 被引量:3

The Compiling of Online Consumer Price Index:Practice Experience,Problem Analysis and Outlook
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摘要 随着居民网络消费的急剧增长,编制居民网络消费价格指数的必要性凸显;但网络消费大数据具有更新快、容量大、频率高、噪音多等特点,传统价格指数理论和方法难以适用。aSPI和1号店指数的编制实践,从源头上优化了基础数据质量,提高了商品篮子的代表性和时效性,指数构造方法更为科学,并开发了系列创新型价格指数,但也存在信息登记标准和规范不统一、大数据技术运用和数据处理不充分、方法和数据发布不完整、理论和方法创新不足等问题。因此,还需要大力促进网络消费数据的共享,系统推动网络消费价格指数理论和方法的创新,积极推进相关统计规范和标准的制定与应用,并进一步强化创新型特色价格指数编制的实践探索。 With the rapid increase of online consumption of the residents,the necessity for compiling online consumer price index becomes urgent,however,the big data of online consumption has the characteristics of rapidly renewing,big volume,high frequency,many noises and so on,therefore,traditional price index theory and method is difficult to be applicable. The compiling practice of aSPI and No.1 Store Index optimizes the quality of basic data from the source and promotes the real-time and representation of commodity basket,and index composition method is more scientific and has developed series of innovative price index but still has the problems in un-uniform standard and regulation for information registration, insufficient application of big data and data processing,incomplete method and data popularization,inadequate innovation of theory and method and so on. Thus,we should boost the sharing of online consumption data,systematically promote the innovation of consumption price index theory and method,actively push forward the preparation and application of relative statistical standard and regulations,and further strengthen practical exploration for the compiling of innovative-style characteristic price index.
作者 陈立双 CHEN Li-shuang(School of Tourism and Hotel Management,Hubei University of Economics,Wuhan 430205,Hubei,China;Key Laboratory for Commercial Big Data Anaiysis and Application of Fujian Province,Minnan Normal University,Zhangzhou 363000,Fujian,China)
出处 《西部论坛》 CSSCI 北大核心 2019年第2期46-52,共7页 West Forum
基金 国家社会科学基金规划项目(16BTJ028)
关键词 网络消费 价格指数 ASPI 1号店指数 CPI 大数据技术 online consumption price index aSPI No.1 Store Index CPI big data technique
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