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Generalized Logarithmic Index Numbers with Demand Shocks: Bridging the Gap between Theory and Practice

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  • Abe, Naohito
  • 阿部, 修人
  • Rao, D.S.Prasada

Abstract

There has long been a gap between theory and practice for measuring aggregate price changes. As Samuelson and Swamy (1974, AER) stated, deflators must satisfy transitivity to make real consumption expenditures consistent with consumer theory. To date, few well-known indices are transitive. Building on Redding and Wenstein (2020), we consider a generalized logarithmic index with demand shocks and establish its monotonicity and transitivity properties, which fills the gap. We also derive conditions under which the logarithmic price index is unique. Analysis of Japanese weekly scanner data shows that the traditional chained Sato-Vartia index has 4.86 percent downward bias per annum.

Suggested Citation

  • Abe, Naohito & 阿部, 修人 & Rao, D.S.Prasada, 2020. "Generalized Logarithmic Index Numbers with Demand Shocks: Bridging the Gap between Theory and Practice," RCESR Discussion Paper Series DP20-1, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hit:rcesrs:dp20-1
    Note: 28 June, 2020
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    References listed on IDEAS

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    1. Samuelson, Paul A & Swamy, S, 1974. "Invariant Economic Index Numbers and Canonical Duality: Survey and Synthesis," American Economic Review, American Economic Association, vol. 64(4), pages 566-593, September.
    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. W. Erwin Diewert, 2022. "Scanner Data, Elementary Price Indexes and the Chain Drift Problem," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 445-606, Springer.
    4. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    5. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    6. Ian Crawford & J. Peter Neary, 2023. "New Characteristics and Hedonic Price Index Numbers," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 665-682, May.
    7. Diewert, Erwin & Marandola, Tina, 2018. "Scanner Data, Elementary Price Indexes and the Chain Drift Problem," Microeconomics.ca working papers tina_marandola-2018-9, Vancouver School of Economics, revised 10 Oct 2018.
    8. Feenstra, Robert C, 1994. "New Product Varieties and the Measurement of International Prices," American Economic Review, American Economic Association, vol. 84(1), pages 157-177, March.
    9. Stephen J Redding & David E Weinstein, 2020. "Measuring Aggregate Price Indices with Taste Shocks: Theory and Evidence for CES Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(1), pages 503-560.
    10. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    11. 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.
    12. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1, May.
    13. Karen E. Dynan, 2000. "Habit Formation in Consumer Preferences: Evidence from Panel Data," American Economic Review, American Economic Association, vol. 90(3), pages 391-406, June.
    14. J. Peter Neary, 2004. "Rationalizing the Penn World Table: True Multilateral Indices for International Comparisons of Real Income," American Economic Review, American Economic Association, vol. 94(5), pages 1411-1428, December.
    15. Robert Martin, 2020. "Changing Tastes Versus Specification Error in Cost-of-Living Measurement," Economic Working Papers 531, Bureau of Labor Statistics.
    16. 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.
    17. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    18. Osborn, Denise R, 1988. "Seasonality and Habit Persistence in a Life Cycle Model of Consumptio n," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(4), pages 255-266, October-D.
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    Cited by:

    1. Abe, Naohito & 阿部, 修人 & Inoue, Toshikatsu & Sato, Hideyasu, 2020. "Price Index Numbers Under Large-Scale Demand Shocks: The Japanese Experience of the COVID-19 Pandemic," RCESR Discussion Paper Series DP20-2, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.

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    More about this item

    Keywords

    Price comparisons; Preference Heterogeneity; Logarithmic Indices; Transitivity; Scanner Data; Chain Drift;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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