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Inflation measurement with high frequency data

Author

Listed:
  • Kevin J. Fox

    (UNSW Sydney)

  • Peter Levell

    (Institute for Fiscal Studies)

  • Martin O'Connell

    (Institute for Fiscal Studies)

Abstract

The availability of large transaction level datasets, such as retail scanner data, provides a wealth of information on prices and quantities that national statistical institutes can use to produce more accurate, timely, measures of inflation. However, there is no universally agreed upon method for calculating price indexes with such high frequency data, reflecting a lack of systematic evidence on the performance of different approaches. We use a dataset that covers 178 product categories comprising all fast-moving consumer goods over 8 years to provide a systematic comparison of the leading bilateral and multilateral index number methods for computing month-to-month inflation.

Suggested Citation

  • Kevin J. Fox & Peter Levell & Martin O'Connell, 2023. "Inflation measurement with high frequency data," IFS Working Papers W23/29, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:ifsewp:23/29
    as

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    File URL: https://ifs.org.uk/sites/default/files/2023-10/WP202329-Inflation-measurement-with-high-frequency-data.pdf
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    References listed on IDEAS

    as
    1. Jessie Handbury & Tsutomu Watanabe & David E. Weinstein, 2013. "How Much Do Official Price Indexes Tell Us about Inflation?," NBER Working Papers 19504, National Bureau of Economic Research, Inc.
    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. Christian Broda & David E. Weinstein, 2010. "Product Creation and Destruction: Evidence and Price Implications," American Economic Review, American Economic Association, vol. 100(3), pages 691-723, June.
    4. Emi Nakamura & Jón Steinsson, 2013. "Price Rigidity: Microeconomic Evidence and Macroeconomic Implications," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 133-163, May.
    5. Inklaar, Robert & Diewert, W. Erwin, 2016. "Measuring industry productivity and cross-country convergence," Journal of Econometrics, Elsevier, vol. 191(2), pages 426-433.
    6. W. Erwin Diewert & Kevin J. Fox, 2022. "Substitution Bias in Multilateral Methods for CPI Construction," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 355-369, January.
    7. Kaplan, Greg & Schulhofer-Wohl, Sam, 2017. "Inflation at the household level," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 19-38.
    8. 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.
    9. Olivier Coibion & Yuriy Gorodnichenko & Rupal Kamdar, 2018. "The Formation of Expectations, Inflation, and the Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 56(4), pages 1447-1491, December.
    10. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers," Economic Journal, Royal Economic Society, vol. 92(365), pages 73-86, March.
    11. Olivier Coibion & Yuriy Gorodnichenko & Gee Hee Hong, 2015. "The Cyclicality of Sales, Regular and Effective Prices: Business Cycle and Policy Implications," American Economic Review, American Economic Association, vol. 105(3), pages 993-1029, March.
    12. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    13. Diewert, W. Erwin & Fox, Kevin J. & de Haan, Jan, 2016. "A newly identified source of potential CPI bias: Weekly versus monthly unit value price indexes," Economics Letters, Elsevier, vol. 141(C), pages 169-172.
    14. Robert J. Hill, 1999. "Comparing Price Levels across Countries Using Minimum-Spanning Trees," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 135-142, February.
    15. 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.
    16. Martin Eichenbaum & Nir Jaimovich & Sergio Rebelo, 2011. "Reference Prices, Costs, and Nominal Rigidities," American Economic Review, American Economic Association, vol. 101(1), pages 234-262, February.
    17. Xavier Jaravel, 2019. "The Unequal Gains from Product Innovations: Evidence from the U.S. Retail Sector," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(2), pages 715-783.
    18. Jaravel, Xavier & O'Connell, Martin, 2020. "Real-time price indices: Inflation spike and falling product variety during the Great Lockdown," Journal of Public Economics, Elsevier, vol. 191(C).
    19. Hill, Robert J, 2001. "Measuring Inflation and Growth Using Spanning Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 167-185, February.
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