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High-Frequency Substitution and the Measurement of Price Indexes

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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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 8176.

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Date of creation: Mar 2001
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Publication status: published as High-Frequency Substitution and the Measurement of Price Indexes , Robert C. Feenstra, Matthew D. Shapiro. in Scanner Data and Price Indexes , Feenstra and Shapiro. 2003
Handle: RePEc:nbr:nberwo:8176

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  1. Roger Betancourt & Margaret Malanoski, 1999. "An Estimable Model of Supermarket Behavior: Prices, Distribution Services and Some Effects of Competition," Empirica, Springer, vol. 26(1), pages 55-73, March.
  2. Diewert, W Erwin, 1978. "Superlative Index Numbers and Consistency in Aggregation," Econometrica, Econometric Society, vol. 46(4), pages 883-900, July.
  3. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
  4. 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.
  5. 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 152-60, April.
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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.).
  5. 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.
  6. 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.).
  7. Junmin Wan, 2005. "Rational Addiction with Optimal Inventories: Theory and Evidence from Cigarette Purchases in Japan," ISER Discussion Paper 0641, Institute of Social and Economic Research, Osaka University.

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