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Understanding Price Variation Across Stores and Supermarket Chains: Some Implications for CPI Aggregation Methods

By Lorraine Ivancic and Kevin J. Fox


The empirical literature on price indices consistently finds that aggregation methods have a considerable impact, particularly when scanner data are used. This paper outlines a novel approach to test for the homogeneity of goods and hence for the appropriateness of aggregation. A hedonic regression framework is used to test for item homogeneity across four supermarket chains and across stores within each of these supermarket chains. We find empirical support for the aggregation of prices across stores which belong to the same supermarket chain. Support was also found for the aggregation of prices across three of the four supermarket chains.Price indexes; aggregation; scanner data; unit values; item homogeneity; hedonics

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  1. (1998). A Guide to Econometrics 4 th Edition.
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  4. (2009). An Index Formula Problem: The Aggregation of Broadly Comparable Items,”
  5. (1997). An overview of research on potential uses of scanner data in the U.S.
  6. (2005). Australian Consumer Price Index: Concepts, Sources and Methods,
  7. (1995). Axiomatic and Economic Approaches to Elementary Price Indexes.”
  8. (1992). Changes in Comparative Price and Changes
  9. (1992). Computing Elementary Aggregates in the Swedish Consumer Price Index.”
  10. (2001). Construction of Price Indexes and Exploration of Biases using Scanner Data.” Paper presented at the Sixth Meeting of the International Working Group on Price Indices,
  11. Consumer Price Manual: Theory and Practice, Geneva: International Labour Organization.
  12. (2009). Eliminating Chain Drift in Price Indexes Based on Scanner Data,” Statistics Netherlands, The Hague.
  13. (1978). Estimating the Dimension of a Model,”
  14. (1976). Exact and Superlative Indexes.”
  15. (1995). Exact Hedonic Price Indexes.” The Review of
  16. (1969). Grafted Polynomials as Approximating Functions,”
  17. (2003). Hedonic Regressions: A Review of Some Unresolved
  18. (1973). Information Theory and an Extension of the Maximum Likelihood Principle,”
  19. (2003). Introductory Econometrics: A Modern Approach,
  20. (1998). Non-parametric Estimation of Technical Progress.”
  21. (1998). On the Use of Unit Value Indices as Consumer Price Subindices.” Paper presented at the
  22. (2005). Pitfalls of Using Unit Values as a Price Measure or Price Index.”
  23. (1994). Price Dispersion, Seller Substitution and the U.S.
  24. (1997). Reconciliation of Consumer Price Index Trends with Corresponding Trends
  25. (2009). Scanner Data, Time Aggregation and the Construction of Price Indexes.”
  26. (2007). The Difference between Hedonic Imputation Indexes and the Time Dummy Hedonic Indexes,”
  27. (2003). The Measurement of Quality-Adjusted Price Changes.”
  28. (1999). Using Scanner Data to Construct CPI Basic Component Indexes.”

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