Abstract
Using online prices has made it possible to estimate inflation rates at a larger scale and higher time resolution than were previously possible. In this chapter, we introduce a daily updated chain index to track prices in over 2400 different product categories and brands over time using data from online price comparison service Prisjakt. Our price index, among others, allows (1) users to optimize purchase decisions in time using historic trends, (2) stores to revise pricing strategies, (3) data analysts to derive business insights at high temporal and categorical resolution, and the index could (4) aid national statistic offices in inflationary studies. By using user–product interaction data as a proxy for quantities sold, products in the index can be weighted by user popularity. Chaining a price index at high frequency, however, presents challenges with potentially large biases, as has been observed in recent studies on scanner data. Here we show that much of these biases can be handled by carefully constructing product weights, without resorting to more involved multilateral indices. Finally, we compare our price index to matching product categories in the official Statistics Sweden consumer price index.
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Notes
- 1.
Sweden, Norway, New Zealand, France, Great Britain, Finland, Denmark (ordered by falling market size).
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Acknowledgements
The author wishes to thank Peter Stark for originally proposing the idea to construct a daily updated price index at Prisjakt. The author wishes to thank Henrik Leijon for valuable suggestions on product weighting. The author wishes to thank Marcin Zawalski for valuable help scaling up the index to cover a greater number of product categories and brands. The author wishes to thank Henrik Leijon, Kevin Fox, Peter Stark, Laura Patel-Smith, Magdalena Henrysson, and Kyle van de Langemheen for valuable comments on the manuscript.
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Rydenfelt, M. (2022). Tracking Price Trends Using User–Product Interaction Data From a Price Comparison Service. In: Tsounis, N., Vlachvei, A. (eds) Advances in Quantitative Economic Research. ICOAE 2021. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-98179-2_27
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