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Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data

Author

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  • W. Erwin Diewert

    (Vancouver School of Economics, University of British Columbia, and School of Economics, UNSW Business School, UNSW Sydney)

  • Kevin J. Fox

    (School of Economics and CAER, UNSW Business School, UNSW Sydney)

Abstract

The use of multilateral indexes is increasingly an accepted approach for incorporating scanner data in a Consumer Price Index. The attractiveness stems from the ability to be able to control for chain drift bias. Consensus on two key issues has yet to be achieved: (i) the best multilateral method to use, and (ii) the best way of extending the resulting series when new observations become available. We present theoretical and simulation evidence on the extent of substitution biases in alternative methods. Our results suggest the use of the CCDI index with a new method, the “mean splice”, for updating.

Suggested Citation

  • W. Erwin Diewert & Kevin J. Fox, 2018. "Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data," Discussion Papers 2018-13, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2018-13
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2018-13.pdf
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    Cited by:

    1. W. Erwin Diewert & Kiyohiko G. Nishimura & Chihiro Shimizu & Tsutomu Watanabe, 2020. "Measuring the Services of Durables and Owner Occupied Housing," Advances in Japanese Business and Economics, in: Property Price Index, chapter 0, pages 223-298, Springer.
    2. W. Erwin Diewert & Robert C. Feenstra, 2021. "Estimating the Benefits of New Products," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 437-473, National Bureau of Economic Research, Inc.
    3. Daniel Melser & Michael Webster, 2021. "Multilateral Methods, Substitution Bias, and Chain Drift: Some Empirical Comparisons," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(3), pages 759-785, September.
    4. Diewert, W, Erwin & Feenstra, Robert, 2017. "Estimating the Benefits and Costs of New and Disappearing Products," Microeconomics.ca working papers tina_marandola-2017-12, Vancouver School of Economics, revised 19 Dec 2017.
    5. Jacek Bia{l}ek & Maciej Berk{e}sewicz, 2020. "Scanner data in inflation measurement: from raw data to price indices," Papers 2005.11233, arXiv.org.
    6. Diewert, Erwin, 2019. "Quality Adjustment and Hedonics: A Unified Approach," Microeconomics.ca working papers erwin_diewert-2019-2, Vancouver School of Economics, revised 14 Mar 2019.
    7. Jacek Białek, 2023. "Improving quality of the scanner CPI: proposition of new multilateral methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2893-2921, June.
    8. Diewert W. Erwin & Fox Kevin J., 2022. "Measuring Inflation under Pandemic Conditions," Journal of Official Statistics, Sciendo, vol. 38(1), pages 255-285, March.
    9. Antonio G. Chessa & Robert Griffioen, 2019. "Comparing Price Indices of Clothing and Footwear for Scanner Data and Web Scraped Data," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 49-68.
    10. W. Erwin Diewert & Kevin J. Fox, 2020. "Measuring Real Consumption and CPI Bias under Lockdown Conditions," NBER Working Papers 27144, National Bureau of Economic Research, Inc.
    11. W. Erwin Diewert & Chihiro Shimizu, 2022. "Residential Property Price Indexes: Spatial Coordinates Versus Neighborhood Dummy Variables," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(3), pages 770-796, September.
    12. Marie Leclair & Isabelle Léonard & Guillaume Rateau & Patrick Sillard & Gaëtan Varlet & Pierre Vernédal, 2019. "Scanner Data: Advances in Methodology and New Challenges for Computing Consumer Price," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 13-29.
    13. 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.
    14. 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.
    15. Jan de Haan & Rens Hendriks & Michael Scholz, 2021. "Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(2), pages 394-417, June.
    16. Bentley Alan, 2022. "Rentals for Housing: A Property Fixed-Effects Estimator of Inflation from Administrative Data," Journal of Official Statistics, Sciendo, vol. 38(1), pages 187-211, March.
    17. Robert J. Hill & Michael Scholz & Chihiro & Miriam Steurer, 2020. "Rolling-Time-Dummy House Price Indexes: Window Length, Linking and Options for Dealing with the Covid-19 Shutdown," Graz Economics Papers 2020-14, University of Graz, Department of Economics.
    18. Zhenkun Zhou & Zikun Song & Tao Ren, 2022. "Predicting China's CPI by Scanner Big Data," Papers 2211.16641, arXiv.org, revised Oct 2023.
    19. Li-Chun Zhang & Ingvild Johansen & Ragnhild Nygaard, 2018. "Tests for price indices in a dynamic item universe," Papers 1808.08995, arXiv.org, revised Oct 2018.
    20. Zhang Li-Chun & Johansen Ingvild & Nygaard Ragnhild, 2019. "Tests for Price Indices in a Dynamic Item Universe," Journal of Official Statistics, Sciendo, vol. 35(3), pages 683-697, September.

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    More about this item

    Keywords

    Consumer Price Index; chain drift; multilateral indexes; Rolling Window indexes; linking methods;
    All these keywords.

    JEL classification:

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

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