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Using Scanner Data to Construct CPI Basic Component Indexes

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  • Reinsdorf, Marshall B

Abstract

This article considers how scanner data could be used in constructing component indexes for the U.S. Consumer Price Index. One product, coffee, in two cities generates over 1.8 million observations in just over two years, so coping with the sheer volume of data would be a challenge. Some other findings are (1) some aggregation of prices into 'unit-value' averages is necessary for practical reasons and to avoid bias, (2) chained Laspeyres indexes are very high, (3) 'modified' Laspeyres indexes have some upward bias but much less than a true Laspeyres index, (4) Fisher ideal or modified Edgeworth indexes perform well, and (5) aggregating prices across outlets to form city-level unit values reduces the discrepancies between index-number formulas.

Suggested Citation

  • Reinsdorf, Marshall B, 1999. "Using Scanner Data to Construct CPI Basic Component Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 152-160, April.
    • Handle: RePEc:bes:jnlbes:v:17:y:1999:i:2:p:152-60
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    Citations

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    Cited by:

    1. Tsutomu Watanabe & Tomoyoshi Yabu, 2018. "The Demand for Money at the Zero Interest Rate Bound," Working Papers on Central Bank Communication 002, University of Tokyo, Graduate School of Economics.
    2. Kota Watanabe & Tsutomu Watanabe, 2014. "We construct a Törnqvist daily price index using Japanese point of sale (POS) scannerdata spanning from 1988 to 2013. We find the following. First, the POS based inflation rate tends to be about 0.5 ," CARF F-Series CARF-F-342, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. Vermeulen, Philip & Gábor, Enikö, 2014. "New evidence on elementary index bias," Working Paper Series 1754, European Central Bank.
    4. Ivancic, Lorraine & Fox, Kevin J., 2013. "Can dissimilarity indexes resolve the issue of when to chain price indexes?," Economics Letters, Elsevier, vol. 118(1), pages 6-9.
    5. Robert J Hill, 2004. "Inflation Measurement for Central Bankers," RBA Annual Conference Volume (Discontinued), in: Christopher Kent & Simon Guttmann (ed.),The Future of Inflation Targeting, Reserve Bank of Australia.
    6. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2021. "Household Inventory, Temporary Sales, and Price Indices," Working Papers on Central Bank Communication 033, University of Tokyo, Graduate School of Economics.
    7. Kota Watanabe & Tsutomu Watanabe, 2014. "Estimating Daily Inflation Using Scanner Data: A Progress Report," UTokyo Price Project Working Paper Series 020, University of Tokyo, Graduate School of Economics.
    8. de Haan, Jan & Diewert, W. Erwin & Fox, Kevin J., 2015. "Weekly versus Monthly Unit Value Price Indexes," Economics working papers erwin_diewert-2015-15, Vancouver School of Economics, revised 20 Jul 2015.
    9. Judith A. Chevalier & Anil K. Kashyap, 2019. "Best Prices: Price Discrimination and Consumer Substitution," American Economic Journal: Economic Policy, American Economic Association, vol. 11(1), pages 126-159, February.
    10. Iqbal Syed & Daniel Melser, 2008. "Prices over the Product Life Cycle: An Empirical Analysis," Discussion Papers 2008-25, School of Economics, The University of New South Wales.
    11. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2020. "Consumer Inventory and the Cost of Living Index: Theory and Some Evidence from Japan," Working Papers on Central Bank Communication 025, University of Tokyo, Graduate School of Economics.
    12. Lorraine Ivancic & Kevin J. Fox, 2013. "Understanding Price Variation Across Stores and Supermarket Chains: Some Implications for CPI Aggregation Methods," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(4), pages 629-647, December.
    13. Jan de Haan & Rens Hendriks & Michael Scholz, 2016. "A Comparison of Weighted Time-Product Dummy and Time Dummy Hedonic Indexes," Graz Economics Papers 2016-13, University of Graz, Department of Economics.
    14. Jan de Haan & Rens Hendriks & Michael Scholz, 2021. "Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(2), pages 394-417, June.
    15. Melser, Daniel & Syed, Iqbal, 2007. "Life Cycle Pricing and the Measurement of Inflation," MPRA Paper 16722, University Library of Munich, Germany, revised 07 Jul 2008.
    16. Robert C. Feenstra & Matthew D. Shapiro, 2003. "High-Frequency Substitution and the Measurement of Price Indexes," NBER Chapters, in: Scanner Data and Price Indexes, pages 123-146, National Bureau of Economic Research, Inc.
    17. Jack E. Triplett, 2003. "Using Scanner Data in Consumer Price Indexes. Some Neglected Conceptual Considerations," NBER Chapters, in: Scanner Data and Price Indexes, pages 151-162, National Bureau of Economic Research, Inc.
    18. Ivancic, Lorraine & Erwin Diewert, W. & Fox, Kevin J., 2011. "Scanner data, time aggregation and the construction of price indexes," Journal of Econometrics, Elsevier, vol. 161(1), pages 24-35, March.
    19. Diewert, W. Erwin & Fox, Kevin J. & de Haan, Jan, 2016. "A newly identified source of potential CPI bias: Weekly versus monthly unit value price indexes," Economics Letters, Elsevier, vol. 141(C), pages 169-172.

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