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Residual seasonality in U.S. GDP data

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  • Phillips, Keith R.

    (Federal Reserve Bank of Dallas)

  • Wang, Jack

    (Federal Reserve Bank of Dallas)

Abstract

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
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    File URL: http://www.dallasfed.org/assets/documents/research/papers/2016/wp1608.pdf
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    References listed on IDEAS

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    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.
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    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.

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