Alternative data sources for high-tech products in the CPI.
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| 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] |
| Copyright of Monthly Labor Review is the property of US Department of Labor and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 182491105 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Alternative data sources for high-tech products in the CPI. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Labor+Review%22">Monthly Labor Review</searchLink>. Dec2024, p1-36. 36p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+virtual+network+operators%22">Mobile virtual network operators</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+behavior%22">Consumer behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Organic+light+emitting+diodes%22">Organic light emitting diodes</searchLink><br /><searchLink fieldCode="DE" term="%22Product+coding%22">Product coding</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Monthly Labor Review is the property of US Department of Labor and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=182491105 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 36 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Mobile virtual network operators Type: general – SubjectFull: Consumer behavior Type: general – SubjectFull: Organic light emitting diodes Type: general – SubjectFull: Product coding Type: general Titles: – TitleFull: Alternative data sources for high-tech products in the CPI. Type: main BibRelationships: IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00981818 Titles: – TitleFull: Monthly Labor Review Type: main |
| ResultId | 1 |
