IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

A Computational Routine for Disaggregating Industry Margin Data to Estimate Product Margin Rates

Listed author(s):
  • Matthew D. Atkinson

    (Bureau of Economic Analysis)

Registered author(s):

    Retail industry product margin rates are used to estimate the retail output proportion of final consumption commodities. The Census Bureau collects data on industry margin rates, but it does not collect product margin rate data. To estimate retail industry-by-commodity output, industry margin rates are disaggregated by product. A number of controls are available for disaggregating industry data. This paper introduces a formal computational method for disaggregating industry margin data using Bayesian statistics and simulation. The routine is capable of accurately imposing multiple controls simultaneously. The method's accuracy is demonstrated by an evaluation of its industry product margin rate estimates. In addition to producing accurate disaggregate estimates, the method is fast and its estimates are replicable. The computational method has a broad range of applications beyond the estimation of industry-by-product margin rates.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    Paper provided by Bureau of Economic Analysis in its series BEA Papers with number 0030.

    in new window

    Date of creation: 2003
    Handle: RePEc:bea:papers:0030
    Contact details of provider: Phone: 202-482-4883
    Web page:

    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Landefeld, J Steven & McCulla, Stephanie H, 2000. "Accounting for Nonmarket Household Production within a National Accounts Framework," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 46(3), pages 289-307, September.
    2. Kendrick, John W, 1979. "Expanding Imputed Values in the National Income and Product Accounts," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 25(4), pages 349-363, December.
    Full references (including those not matched with items on IDEAS)

    This item is featured on the following reading lists or Wikipedia pages:

    1. Papers and articles using the American Time Use Survey (ATUS)

    When requesting a correction, please mention this item's handle: RePEc:bea:papers:0030. 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: (Bryn Whitmire)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.