Description
Title: Utilizing Web Scraping Techniques to Calculate High-Frequency Sub-National Spatial Consumer Price Indexes
Abstract: the advancement of communications and information Technology and digital economies have altered how goods and services are consumed in many spheres of life, which has a bearing on users’ rising expectations regarding price statistics. As a result, it is critical to promptly disseminate information on variations in consumer prices over time and space. The quality and effectiveness of consumer price indices could be greatly enhanced by the use of web scraped data, which is the process of gathering sizable amounts of data from the web. In this study, we investigate how the time-interaction-region product model can be used to create high-frequency price indices for groups of products using web scraped data. In 11 US cities included in our dataset, we calculated the monthly average prices of five basic goods using the Consumer Price Index for All Urban Consumers (CPI-U) classification and followed the changes in those prices over time. Results demonstrate how web scraping data may provide timely estimates of sub-national SPI evolution and reveal seasonal trends for particular categories, even though our dataset only covers a small portion of the CPI-U index.
Keywords: consumer spatial price indexes; data scraping; spatial index; time comparison; big data
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Economics
Writer Experience: 20+ Years
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