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The FEWS Index: Fixed Effects with a Window Splice

Author

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  • Krsinich Frances

    (Statistics New Zealand – Prices Unit, PO Box 2922 Wellington 6041, New Zealand.)

Abstract

This article describes the estimation of quality-adjusted price indexes from ‘big data’ such as scanner and online data when there is no available information on product characteristics for explicit quality adjustment using hedonic regression. The longitudinal information can be exploited to implicitly quality-adjust the price indexes. The fixed-effects (or ‘time-product dummy’) index is shown to be equivalent to a fully interacted time-dummy hedonic index based on all price-determining characteristics of the products, despite those characteristics not being observed. In production, this can be combined with a modified approach to splicing that incorporates the price movement across the full estimation window to reflect new products with one period’s lag without requiring revision. Empirical results for this fixed-effects window-splice (FEWS) index are presented for different data sources: three years of New Zealand consumer electronics scanner data from market-research company GfK; six years of United States supermarket scanner data from market-research company IRI; and 15 months of New Zealand consumer electronics daily online data from MIT’s Billion Prices Project.

Suggested Citation

  • Krsinich Frances, 2016. "The FEWS Index: Fixed Effects with a Window Splice," Journal of Official Statistics, Sciendo, vol. 32(2), pages 375-404, June.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:2:p:375-404:n:9
    DOI: 10.1515/jos-2016-0021
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    References listed on IDEAS

    as
    1. Jan de Haan & Frances Krsinich, 2014. "Scanner Data and the Treatment of Quality Change in Nonrevisable Price Indexes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 341-358, July.
    2. de Haan, Jan & van der Grient, Heymerik A., 2011. "Eliminating chain drift in price indexes based on scanner data," Journal of Econometrics, Elsevier, vol. 161(1), pages 36-46, March.
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    Cited by:

    1. Alberto Cavallo, 2017. "Are Online and Offline Prices Similar? Evidence from Large Multi-channel Retailers," American Economic Review, American Economic Association, vol. 107(1), pages 283-303, January.
    2. Erica L. Groshen & Brian C. Moyer & Ana M. Aizcorbe & Ralph Bradley & David M. Friedman, 2017. "How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 187-210, Spring.

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