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

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  • Paul Armknecht
  • Mick Silver

Abstract

type="main"> Consumer price indexes (CPIs) are commonly 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 quite simple variants of a hybrid formula suggested by Lent and Dorfman that use the same data as Laspeyres-type indexes but substantially reduce their bias.

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  • Paul Armknecht & Mick Silver, 2014. "Post-Laspeyres: The Case for a New Formula for Compiling Consumer Price Indexes," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(2), pages 225-244, June.
    • Handle: RePEc:bla:revinw:v:60:y:2014:i:2:p:225-244
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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Shang, Yizi & Lu, Shibao & Shang, Ling & Li, Xiaofei & Shi, Hongwang & Li, Wei, 2017. "Decomposition of industrial water use from 2003 to 2012 in Tianjin, China," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 53-61.
    7. Dilip M. Nachane & Aditi Chaubal, 2017. "The Plutocratic Bias in the Indian CPI," Working Papers id:12106, eSocialSciences.
    8. 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.
    9. 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.
    10. Mick Silver, 2012. "Why House Price Indexes Differ: Measurement and Analysis," IMF Working Papers 2012/125, International Monetary Fund.
    11. 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.
    12. 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.

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