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Integrating scanner data in Consumer Price Index calculation: Consumer Price Index calculation using scanner data
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| 1.11 MB | Adobe PDF |
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Resumo(s)
The conducted research is a work project of Nova IMS student employed by Statistics Lithuania
(National Statistics Office). The primary purpose of this research is to propose an alternative
methodology for Consumer Price Index calculation using new data sources, which will contribute to
national and international level consumer price statistics.
The current methodology for consumer price index calculation is based on Laspeyers method for
index calculation, which was last updated in 2004. Two decades ago, the supply of scanner data was
minimal. Therefore the methodology was based on physical price collectors and survey statistics.
Such methodology is known to be extremely costly and inefficient. This research will investigate the
possibilities of incorporating new data sources in the consumer price statistics and investigate the
alternative index calculation methods that could potentially eliminate the old model, which is known
to struggle with bias sampling and chain drift problems. The literature review will cover these issues
thoroughly and present possible multilateral or bilateral index alternatives to counter them.
The research will also cover the classification and sampling procedures necessary to construct timeseries
data. Obtained multilateral and bilateral index values are then compared against the current
methodology, followed by conclusions and discussion sections.
The research does not only consider scanner data but also explicitly discusses the applications of
web-scraped data. The results section reveals that Jevons and GEKS index values are not correlated,
indicating that sales turnover may not be rationally correlated with price movements. That is valid
evidence that web-scraped data can also be beneficial in consumer price index calculation and official
statistics as a supplementary source of information.
Descrição
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Consumer Price Index Bilateral Multilateral Scanner data Machine Learning Classification