Hard Numbers: Open Consumer Price Database

Author:

Isakov Alex, ,Latypov Rodion,Repin Andrey,Postolit Egor,Evseev Alexey,Sinelnikova-Muryleva ElenaORCID, , , , ,

Abstract

We document a new source of consumer price microdata. The new database allows researchers studying consumer price behaviour to access current and granular raw statistical observations. The range of observed prices fully covers goods and services of the Rosstat’s CPI sample and extends beyond it. In this paper, we pursue two objectives. First, we describe the data collection mechanism, data structure, and their access protocols, as well provide four complete illustrations of their application using open API: i) training machine models of product classification based on text labels, ii) real-time tracking of product prices, iii) estimating hedonic regressions for product groups, and iv) calculating arbitrary analytical price indices. Second, we share a set of basic skills and technologies for the benefit of researchers interested in creating their own sources of alternative data.

Publisher

The Central Bank of the Russian Federation

Reference27 articles.

1. 1. Aizcorbe, A., Byrne, D. M. and Sichel, D. E. (2020). Getting Smart about Phones: New Price Indexes and the Allocation of Spending between Devices and Services Plans in Personal Consumption Expenditures. In: B. M. Fraumeni, ed. Measuring Economic Growth and Productivity. Elsevier, pp. 387-411. https://doi.org/10.1016/B978-0-12-817596-5.00017-2

2. 2. Barcaroli, G., Scannapieco, M., Scarno, M. and Summa, D. (2015). Using Internet as a Data Source for Official Statistics: A Comparative Analysis of Web Scraping Technologies. In: New Techniques and Technologies for Statistics 2015 (NTTS 2015) - Reliable Evidence for a Society in Transition. Eurostat. https://ec.europa.eu/eurostat/ cros/system/files/NTTS2015%20proceedings.pdf [accessed on 22 January 2021]

3. 3. Bhardwaj, H., Flower, T., Lee, P. and Mayhew, M. (2017). Research Indices Using Web Scraped Price Data. https://www.ons.gov.uk/economy/inflationandpriceindices/ articles/researchindicesusingwebscrapedpricedata/august2017update [accessed on 22 January 2021]

4. 4. Boettcher, I. (2015). Big Data in Price Statistics - Scanner Data and Web-Scraping. http://www.osg.or.at/download/files/%7BAB458C9E-20F7-4208-BE75- D340BFF444FE%7D/21_Ingolf_Boettcher.pdf [accessed on 22 January 2021]

5. 5. Cavallo, A. (2013). Online vs Official Price Indexes: Measuring Argentina's Inflation. Journal of Monetary Economics, 60(2), pp. 152-165. https://doi.org/10.1016/j.jmoneco.2012.10.002

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3