IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1906.11208.html
   My bibliography  Save this paper

Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big data statistics

Author

Listed:
  • Li-Chun Zhang

Abstract

Purchase data from retail chains provide proxy measures of private household expenditure on items that are the most troublesome to collect in the traditional expenditure survey. Due to the sheer amount of proxy data, the bias due to coverage and selection errors completely dominates the variance. We develop tests for bias based on audit sampling, which makes use of available survey data that cannot be linked to the proxy data source at the individual level. However, audit sampling fails to yield a meaningful mean squared error estimate, because the sampling variance is too large compared to the bias of the big data estimate. We propose a novel accuracy measure that is applicable in such situations. This can provide a necessary part of the statistical argument for the uptake of big data source, in replacement of traditional survey sampling. An application to disaggregated food price index is used to demonstrate the proposed approach.

Suggested Citation

  • Li-Chun Zhang, 2019. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big data statistics," Papers 1906.11208, arXiv.org.
    • Handle: RePEc:arx:papers:1906.11208
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1906.11208
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erich Battistin & Mario Padula, 2016. "Survey instruments and the reports of consumption expenditures: evidence from the consumer expenditure surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 559-581, February.
    Full references (including those not matched with items on IDEAS)

    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. Olivier Coibion & Yuriy Gorodnichenko & Dmitri Koustas, 2021. "Consumption Inequality and the Frequency of Purchases," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 449-482, October.
    2. Rodolfo G. Campos & Iliana Reggio & Dionisio Garc𫑐, 2013. "Micro versus macro consumption data: the cyclical properties of the consumer expenditure survey," Applied Economics, Taylor & Francis Journals, vol. 45(26), pages 3778-3785, September.
    3. Giacomo De Giorgi & Luca Gambetti, 2012. "Consumption Heterogeneity over the Business Cycle," Working Papers 646, Barcelona School of Economics.
    4. Olga Gorbachev, 2011. "Did Household Consumption Become More Volatile?," American Economic Review, American Economic Association, vol. 101(5), pages 2248-2270, August.
    5. Li‐Chun Zhang, 2021. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 571-588, April.
    6. Campos, Rodolfo G. & Reggio, Iliana, 2013. "Measurement error and imputation of consumption in survey data," UC3M Working papers. Economics we1219, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Giacomo De Giorgi & Luca Gambetti, 2012. "The Effects of Government Spending on the Distribution of Consumption," Working Papers 645, Barcelona School of Economics.
    8. Giacomo De Giorgi & Luca Gambetti, 2017. "Business Cycle Fluctuations and the Distribution of Consumption," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 23, pages 19-41, January.
    9. Scrimgeour, Dean & Gorry, James, 2015. "Using Engel Curves to Estimate CPI Bias for the Elderly," Working Papers 2015-03, Department of Economics, Colgate University, revised 08 Jun 2015.
    10. Campos, Rodolfo G. & Reggio, Iliana, 2014. "Measurement error in imputation procedures," Economics Letters, Elsevier, vol. 122(2), pages 197-202.
    11. Justine Hastings & Jesse M. Shapiro, 2018. "How Are SNAP Benefits Spent? Evidence from a Retail Panel," American Economic Review, American Economic Association, vol. 108(12), pages 3493-3540, December.
    12. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 23-50, National Bureau of Economic Research, Inc.
    13. Campos, Rodolfo G. & Reggio, Iliana & García-Píriz, Dionisio, 2012. "Micro vs. macro consumption data : the cyclical properties of the consumer expenditure survey," UC3M Working papers. Economics we1220, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Marcin Hitczenko, 2013. "Optimal recall period length in consumer payment surveys," Working Papers 13-16, Federal Reserve Bank of Boston.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1906.11208. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.