IDEAS home Printed from
   My bibliography  Save this paper

Residual seasonality in U.S. GDP data


  • Phillips, Keith R.

    (Federal Reserve Bank of Dallas)

  • Wang, Jack

    (Federal Reserve Bank of Dallas)


Rudebush et al (2015a, b) and the Bureau of Economic Analysis find the presence of residual seasonality in the official estimates of U.S. real gross domestic product (GDP). Directly seasonally adjusting official seasonally adjusted GDP, which we refer to as double seasonal adjustment, could revise the first quarter growth in the past several years upward by an average of about 1.5 percentage points. The presence of residual seasonality can significantly distort current analysis of national and regional economies. In this paper we look more closely at the U.S. GDP data and study the quality of the seasonal adjustment when it is applied to data that has already been indirectly seasonally adjusted. We find that double seasonal adjustment can lead to estimates that are of moderate quality. While the optimal method would be to directly seasonally adjust the aggregate not seasonally adjusted data, if this is not possible, double seasonally adjusted data would likely lead to better estimates.

Suggested Citation

  • Phillips, Keith R. & Wang, Jack, 2016. "Residual seasonality in U.S. GDP data," Working Papers 1608, Federal Reserve Bank of Dallas.
  • Handle: RePEc:fip:feddwp:1608
    DOI: 10.24149/wp1608

    Download full text from publisher

    File URL:
    File Function: Full text
    Download Restriction: no

    References listed on IDEAS

    1. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    2. Rudebusch, Glenn D. & Wilson, Daniel J. & Mahedy, Tim, 2015. "The puzzle of weak first-quarter GDP growth," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Bok, Brandyn & Caratelli, Daniele & Giannone, Domenico & Sbordone, Argia M. & Tambalotti, Andrea, 2017. "Macroeconomic nowcasting and forecasting with big data," Staff Reports 830, Federal Reserve Bank of New York.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:fip:feddwp:1608. 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: (Amy Chapman). General contact details of provider: .

    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 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.

    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.