IDEAS home Printed from https://ideas.repec.org/p/swe/wpaper/2018-13.html
   My bibliography  Save this paper

Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2018-13.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Robert J. Hill, 2004. "Constructing Price Indexes across Space and Time: The Case of the European Union," American Economic Review, American Economic Association, vol. 94(5), pages 1379-1410, December.
    2. Kevin Fox & Robert Hill & W. Diewert, 2004. "Identifying Outliers in Multi-Output Models," Journal of Productivity Analysis, Springer, vol. 22(1), pages 73-94, July.
    3. 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.
    4. Allen, Robert C & Diewert, W Erwin, 1981. "Direct versus Implicit Superlative Index Number Formulae," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 430-435, August.
    5. Erwin Diewert, 2005. "Weighted Country Product Dummy Variable Regressions And Index Number Formulae," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 51(4), pages 561-570, December.
    6. Balk,Bert M., 2012. "Price and Quantity Index Numbers," Cambridge Books, Cambridge University Press, number 9781107404960.
    7. 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.
    8. Robert J. Hill, 1997. "A Taxonomy Of Multilateral Methods For Making International Comparisons Of Prices And Quantities," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 43(1), pages 49-69, March.
    9. D.S. Prasada Rao, 2004. "The Country-Product-Dummy Method: A Stochastic Approach to the Computation of Purchasing Power Parities in the ICP," CEPA Working Papers Series WP032004, School of Economics, University of Queensland, Australia.
    10. D. S.P. Rao (ed.), 2009. "Purchasing Power Parities of Currencies," Books, Edward Elgar Publishing, number 3725.
    11. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Introduction to "Scanner Data and Price Indexes"," NBER Chapters, in: Scanner Data and Price Indexes, pages 1-13, National Bureau of Economic Research, Inc.
    12. 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.
    13. 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.
    14. W. Erwin Diewert, 1999. "Axiomatic and Economic Approaches to International Comparisons," NBER Chapters, in: International and Interarea Comparisons of Income, Output, and Prices, pages 13-107, National Bureau of Economic Research, Inc.
    15. Sato, Kazuo, 1976. "The Ideal Log-Change Index Number," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 223-228, May.
    16. 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.
    17. D. S. Prasada Rao, 2005. "On The Equivalence Of Weighted Country‐Product‐Dummy (Cpd) Method And The Rao‐System For Multilateral Price Comparisons," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 51(4), pages 571-580, December.
    18. Inklaar, Robert & Diewert, W. Erwin, 2016. "Measuring industry productivity and cross-country convergence," Journal of Econometrics, Elsevier, vol. 191(2), pages 426-433.
    19. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    20. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Diewert W. Erwin & Fox Kevin J., 2022. "Measuring Inflation under Pandemic Conditions," Journal of Official Statistics, Sciendo, vol. 38(1), pages 255-285, March.
    5. 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 Études Économiques (INSEE), issue 509, pages 13-29.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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 Études Économiques (INSEE), issue 509, pages 49-68.
    13. 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.
    14. Diewert, Erwin & Shimizu, Chihiro, 2019. "Residential Property Price Indexes: Spatial Coordinates versus Neighbourhood Dummy Variables," Microeconomics.ca working papers erwin_diewert-2019-11, Vancouver School of Economics, revised 10 Jan 2020.
    15. Diewert, Erwin, 2018. "Scanner Data, Elementary Price Indexes and the Chain Drift Problem," Microeconomics.ca working papers erwin_diewert-2018-10, Vancouver School of Economics, revised 25 Oct 2018.
    16. 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.
    17. 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.
    18. Zhenkun Zhou & Zikun Song & Tao Ren, 2022. "Predicting China's CPI by Scanner Big Data," Papers 2211.16641, arXiv.org.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diewert, Erwin, 2018. "Scanner Data, Elementary Price Indexes and the Chain Drift Problem," Microeconomics.ca working papers erwin_diewert-2018-10, Vancouver School of Economics, revised 25 Oct 2018.
    2. 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.
    3. Diewert, W. Erwin & Fox, Kevin J., 2016. "Kevin J. Fox Interview of W. Erwin Diewert," Microeconomics.ca working papers erwin_diewert-2016-6, Vancouver School of Economics, revised 02 Jun 2016.
    4. Diewert, Erwin, 2008. "New Methodology for Linking the Regions," Economics working papers erwin_diewert-2008-9, Vancouver School of Economics, revised 09 Sep 2008.
    5. Barnett, William A. & Erwin Diewert, W. & Zellner, Arnold, 2011. "Introduction to measurement with theory," Journal of Econometrics, Elsevier, vol. 161(1), pages 1-5, March.
    6. 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.
    7. Adam Gorajek, 2018. "Econometric Perspectives on Economic Measurement," RBA Research Discussion Papers rdp2018-08, Reserve Bank of Australia.
    8. Gholamreza Hajargasht, 2022. "Reliability of Ideal Indexes," Papers 2210.13684, arXiv.org.
    9. Li, Qingxiao & Cakir, Metin, 2020. "Thrifty Food Plan Panel Price Index and the Real Value of SNAP Benefits," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304201, Agricultural and Applied Economics Association.
    10. 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.
    11. Erwin Diewert, 2010. "New Methodological Developments For The International Comparison Program," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(s1), pages 11-31, June.
    12. Ludwig Auer, 2012. "Räumliche Preisvergleiche: Aggregationskonzepte und Forschungsperspektiven," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(1), pages 27-56, December.
    13. 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.
    14. Hill, Robert J. & Timmer, Marcel P., 2006. "Standard Errors as Weights in Multilateral Price Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 366-377, July.
    15. 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.
    16. repec:dgr:rugggd:200473 is not listed on IDEAS
    17. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    18. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2021. "Household Inventory, Temporary Sales, and Price Indices," CARF F-Series CARF-F-520, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    19. Abe, Naohito & Inakura, Noriko & Tonogi, Akiyuki, 2017. "Effects of the Entry and Exit of Products on Price Indexes," RCESR Discussion Paper Series DP17-2, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    20. Abe, Naohito & Rao, D.S. Prasada, 2022. "Towards a simplified approach to international price comparisons: A case for the Multilateral Walsh Index," RCESR Discussion Paper Series DP22-1, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    21. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2020. "Consumer Inventory and the Cost of Living Index: Theory and Some Evidence from Japan," Working Papers on Central Bank Communication 025, University of Tokyo, Graduate School of Economics.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:swe:wpaper:2018-13. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/senswau.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Hongyi Li (email available below). General contact details of provider: https://edirc.repec.org/data/senswau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.