IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/14280.html
   My bibliography  Save this book chapter

Big Data in the US Consumer Price Index: Experiences and Plans

In: Big Data for Twenty-First-Century Economic Statistics

Author

Listed:
  • Crystal G. Konny
  • Brendan K. Williams
  • David M. Friedman

Abstract

No abstract is available for this item.

Suggested Citation

  • Crystal G. Konny & Brendan K. Williams & David M. Friedman, 2019. "Big Data in the US Consumer Price Index: Experiences and Plans," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 69-98, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14280
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c14280.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Melser & Iqbal A. Syed, 2016. "Life Cycle Price Trends and Product Replacement: Implications for the Measurement of Inflation," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 509-533, September.
    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. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    4. Brendan Williams & Erick Sager, 2019. "A New Vehicles Transaction Price Index: Offsetting the Effects of Price Discrimination and Product Cycle Bias with a Year-Over-Year Index," Economic Working Papers 514, Bureau of Labor Statistics.
    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. David M. Byrne, 2022. "The Digital Economy and Productivity," Finance and Economics Discussion Series 2022-038, Board of Governors of the Federal Reserve System (U.S.).
    2. Xavier Jaravel & Erick Sager, 2019. "What are the price effects of trade? Evidence from the US and implications for quantitative trade models," CEP Discussion Papers dp1642, Centre for Economic Performance, LSE.
    3. Carolyn Wolff & Randall Lutter, 2020. "Why are pharmacy acquisition costs and consumer prescription drug price indices apparently diverging?," Health Economics, John Wiley & Sons, Ltd., vol. 29(12), pages 1721-1727, December.
    4. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2021. "Household Inventory, Temporary Sales, and Price Indices," Working Papers on Central Bank Communication 033, University of Tokyo, Graduate School of Economics.
    5. Ilaria Benedetti & Tiziana Laureti & Luigi Palumbo & Brandon M. Rose, 2022. "Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques," Economies, MDPI, vol. 10(4), pages 1-20, April.
    6. Jaravel, Xavier & Sager, Erick, 2019. "What are the price effects of trade? Evidence from the US for quantitative trade models," LSE Research Online Documents on Economics 103402, London School of Economics and Political Science, LSE Library.
    7. Alex Isakov & Rodion Latypov & Andrey Repin & Egor Postolit & Alexey Evseev & Elena Sinelnikova-Muryleva, 2021. "Hard Numbers: Open Consumer Price Database," Russian Journal of Money and Finance, Bank of Russia, vol. 80(1), pages 104-119, March.

    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. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2016. "Product Turnover and Deflation: Evidence from Japan," UTokyo Price Project Working Paper Series 073, University of Tokyo, Graduate School of Economics.
    2. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    3. Bogdan Oancea, 2023. "Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics," Mathematics, MDPI, vol. 11(7), pages 1-32, March.
    4. 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.
    5. 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.
    6. Zhenkun Zhou & Zikun Song & Tao Ren, 2023. "A New Weighted Food CPI from Scanner Big Data in China," Papers 2310.04242, arXiv.org, revised Oct 2023.
    7. Erica L. Groshen & Brian C. Moyer & Ana M. Aizcorbe & Ralph Bradley & David M. Friedman, 2017. "How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 187-210, Spring.
    8. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Suhas Vijaykunar & Shan Wan, 2023. "Hedonic prices and quality adjusted price indices powered by AI," CeMMAP working papers 08/23, Institute for Fiscal Studies.
    9. Kozo Ueda & Kota Watanabe & Tsutomu Watanabe, 2018. "Product Turnover and the Cost of Living Index: Quality vs. Fashion Effects," Globalization Institute Working Papers 337, Federal Reserve Bank of Dallas.
    10. Daniel Melser, 2019. "Valuing the quantity and quality of product variety to consumers," Empirical Economics, Springer, vol. 57(6), pages 2107-2128, December.
    11. Zhenkun Zhou & Zikun Song & Tao Ren, 2022. "Predicting China's CPI by Scanner Big Data," Papers 2211.16641, arXiv.org, revised Oct 2023.
    12. Jan de Haan & Rens Hendriks & Michael Scholz, 2016. "A Comparison of Weighted Time-Product Dummy and Time Dummy Hedonic Indexes," Graz Economics Papers 2016-13, University of Graz, Department of Economics.
    13. Nobuhiro Abe & Kimiaki Shinozaki, 2018. "Compilation of Experimental Price Indices Using Big Data and Machine Learning:A Comparative Analysis and Validity Verification of Quality Adjustments," Bank of Japan Working Paper Series 18-E-13, Bank of Japan.
    14. Fernando Alvarez & Francesco Lippi & Juan Passadore, 2017. "Are State- and Time-Dependent Models Really Different?," NBER Macroeconomics Annual, University of Chicago Press, vol. 31(1), pages 379-457.
    15. Diewert, Erwin & Shimizu, Chihiro, 2015. "Residential Property Price Indices For Tokyo," Macroeconomic Dynamics, Cambridge University Press, vol. 19(8), pages 1659-1714, December.
    16. Diewert, W. Erwin & Fox, Kevin J., 2017. "Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data," Microeconomics.ca working papers erwin_diewert-2017-3, Vancouver School of Economics, revised 23 Mar 2017.
    17. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    18. Jennifer Peña & Elvira Prades, 2021. "Price setting in Chile: Micro evidence from consumer on-line prices during the social outbreak and Covid-19," Working Papers 2112, Banco de España.
    19. Mauro Caselli & Arpita Chatterjee & Shengyu Li, 2023. "Productivity and Quality of Multi-product Firms," Discussion Papers 2023-10, School of Economics, The University of New South Wales.
    20. 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).

    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

    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:nbr:nberch:14280. See general information about how to correct material in RePEc.

    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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.