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High-Frequency Substitution and the Measurement of Price Indexes

Author

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  • Robert C. Feenstra
  • Matthew D. Shapiro
Abstract
This paper investigates the use of high-frequency scanner data to construct price indexes. In the presence of inventory behavior, purchases and consumption by individuals differ over time. Cost-of-living indexes can still be constructed using data on purchases. For weekly data on canned tuna, the paper contrast two different types of price indexes: fixed-base and chained indexes. Only the former are theoretically correct, and in fact, the chained indexes have a pronounced upward bias for most regions of the U.S. This upward bias can be caused by consumers purchasing goods for inventory. The paper presents some direct statistical support for inventory behavior being the cause of the upward bias, though advertising and special displays also have a very significant impact on shopping patterns.

Suggested Citation

  • Robert C. Feenstra & Matthew D. Shapiro, 2001. "High-Frequency Substitution and the Measurement of Price Indexes," NBER Working Papers 8176, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:8176
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    References listed on IDEAS

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    1. Betancourt, Roger R. & Gautschi, David, 1992. "The demand for retail products and the household production model : New views on complementarity and substitutability," Journal of Economic Behavior & Organization, Elsevier, vol. 17(2), pages 257-275, March.
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    5. Diewert, W Erwin, 1978. "Superlative Index Numbers and Consistency in Aggregation," Econometrica, Econometric Society, vol. 46(4), pages 883-900, July.
    6. 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.
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    Cited by:

    1. Ballestar, María Teresa & Díaz-Chao, Ángel & Sainz, Jorge & Torrent-Sellens, Joan, 2021. "Impact of robotics on manufacturing: A longitudinal machine learning perspective," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. 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.
    3. Nakamura, Alice O. & Nakamura, Emi & Nakamura, Leonard I., 2011. "Price dynamics, retail chains and inflation measurement," Journal of Econometrics, Elsevier, vol. 161(1), pages 47-55, March.
    4. 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.
    5. Thierry Magnac & Pierre Dubois, 2016. "Consumer Demand with Unobserved Stockpiling and Intertemporal Price Discrimination," 2016 Meeting Papers 451, Society for Economic Dynamics.
    6. Junmin Wan, 2004. "Rational Addiction with Optimal Inventories: Theory and Evidence from Cigarette Purchases in Japan," Discussion Papers in Economics and Business 04-01-Rev, Osaka University, Graduate School of Economics, revised Feb 2006.
    7. Rachel Griffith & Ephraim Leibtag & Andrew Leicester & Aviv Nevo, 2009. "Consumer Shopping Behavior: How Much Do Consumers Save?," Journal of Economic Perspectives, American Economic Association, vol. 23(2), pages 99-120, Spring.
    8. David E. Lebow & Jeremy B. Rudd, 2001. "Measurement error in the consumer price index: where do we stand?," Finance and Economics Discussion Series 2001-61, Board of Governors of the Federal Reserve System (U.S.).
    9. Fox, Kevin J. & Syed, Iqbal A., 2016. "Price discounts and the measurement of inflation," Journal of Econometrics, Elsevier, vol. 191(2), pages 398-406.
    10. Glandon, PJ, 2018. "Sales and the (Mis)measurement of price level fluctuations," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 60-77.
    11. David E. Lebow & Jeremy B. Rudd, 2006. "Inflation measurement," Finance and Economics Discussion Series 2006-43, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation

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