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Post-Laspeyres: The Case for a New Formula for Compiling Consumer Price Indexes

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  • Mr. Paul A Armknecht Jr.
  • Mick Silver

Abstract

Consumer price indexes (CPIs) are compiled at the higher (weighted) level using Laspeyres-type arithmetic averages. This paper questions the suitability of such formulas and considers two counterpart alternatives that use geometric averaging, the Geometric Young and the (price-updated) Geometric Lowe. The paper provides a formal decomposition and understanding of the differences between the two. Empirical results are provided using United States CPI data. The findings lead to an advocacy of variants of a hybrid formula suggested by Lent and Dorfman (2009) that substantially reduces bias from Laspeyres-type indexes.

Suggested Citation

  • Mr. Paul A Armknecht Jr. & Mick Silver, 2012. "Post-Laspeyres: The Case for a New Formula for Compiling Consumer Price Indexes," IMF Working Papers 2012/105, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2012/105
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    Cited by:

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    3. Dilip M. Nachane & Aditi Chaubal, 2017. "The Plutocratic Bias in the Indian CPI," Working Papers id:12106, eSocialSciences.
    4. William D. Larson & Justin Contat, 2021. "Transaction Composition and House Price Index Measurement: Evidence from a Repeat-Sales Aggregation Index," FHFA Staff Working Papers 21-01, Federal Housing Finance Agency.
    5. Deutsch Tomi, 2016. "Statistical Capacity Building of Official Statisticians in Practice: Case of the Consumer Price Index," Journal of Official Statistics, Sciendo, vol. 32(4), pages 827-848, December.
    6. Jianchang Lu & Weiguo Fan & Ming Meng, 2015. "Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors," Energies, MDPI, vol. 8(4), pages 1-25, April.
    7. 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.
    8. 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.
    9. Herzberg, Julika & Knetsch, Thomas A. & Schwind, Patrick & Weinand, Sebastian, 2021. "Quantifying bias and inaccuracy of upper-level aggregation in HICPs for Germany and the euro area," Discussion Papers 06/2021, Deutsche Bundesbank.
    10. Wang, Minggang & Tian, Lixin & Xu, Hua & Li, Weiyu & Du, Ruijin & Dong, Gaogao & Wang, Jie & Gu, Jiani, 2017. "Systemic risk and spatiotemporal dynamics of the consumer market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 188-204.
    11. Xiao, Jiang & Wang, Minggang & Tian, Lixin & Zhen, Zaili, 2018. "The measurement of China’s consumer market development based on CPI data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 664-680.
    12. Mick Silver, 2012. "Why House Price Indexes Differ: Measurement and Analysis," IMF Working Papers 2012/125, International Monetary Fund.

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