Alternative data sources for high-tech products in the CPI.

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Bibliographic Details
Title:	Alternative data sources for high-tech products in the CPI.
Source:	Monthly Labor Review. Dec2024, p1-36. 36p.
Subject Terms:	*Machine learning, Mobile virtual network operators, Consumer behavior, Organic light emitting diodes, Product coding
Abstract:	The article explores the use of alternative data sources to enhance price-change measurement for high-tech goods and services in the U.S. Consumer Price Index (CPI). The U.S. Bureau of Labor Statistics (BLS) is working towards incorporating nontraditional data collection methods for items like televisions and wireless telephone services to improve quality change and adjustment issues. By utilizing hedonic imputation methods and Törnqvist aggregation, the study shows that faster reactions to price shocks and better constant quality price change measures can be achieved for these products. The research delves into the impact of various characteristics on prices for televisions and wireless telephone services, utilizing machine learning techniques and alternative data sources to ensure accurate representation of changing consumer preferences and technological advancements in CPI calculations. [Extracted from the article]
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Database:	Education Research Complete

Description
Abstract:	The article explores the use of alternative data sources to enhance price-change measurement for high-tech goods and services in the U.S. Consumer Price Index (CPI). The U.S. Bureau of Labor Statistics (BLS) is working towards incorporating nontraditional data collection methods for items like televisions and wireless telephone services to improve quality change and adjustment issues. By utilizing hedonic imputation methods and Törnqvist aggregation, the study shows that faster reactions to price shocks and better constant quality price change measures can be achieved for these products. The research delves into the impact of various characteristics on prices for televisions and wireless telephone services, utilizing machine learning techniques and alternative data sources to ensure accurate representation of changing consumer preferences and technological advancements in CPI calculations. [Extracted from the article]
ISSN:	00981818