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Multivariate singular spectrum analysis for forecasting revisions to real-time data

Author

Listed:
  • Kerry Patterson
  • Hossein Hassani
  • Saeed Heravi
  • Anatoly Zhigljavsky

Abstract

Real-time data on national accounts statistics typically undergo an extensive revision process, leading to multiple vintages on the same generic variable. The time between the publication of the initial and final data is a lengthy one and raises the question of how to model and forecast the final vintage of data - an issue that dates from seminal articles by Mankiw et al. [51], Mankiw and Shapiro [52] and Nordhaus [57]. To solve this problem, we develop the non-parametric method of multivariate singular spectrum analysis (MSSA) for multi-vintage data. MSSA is much more flexible than the standard methods of modelling that involve at least one of the restrictive assumptions of linearity, normality and stationarity. The benefits are illustrated with data on the UK index of industrial production: neither the preliminary vintages nor the competing models are as accurate as the forecasts using MSSA.

Suggested Citation

  • Kerry Patterson & Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2011. "Multivariate singular spectrum analysis for forecasting revisions to real-time data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2183-2211.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2183-2211
    DOI: 10.1080/02664763.2010.545371
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    References listed on IDEAS

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    1. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    2. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
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    Citations

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    Cited by:

    1. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    2. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    3. Moody Chu & Matthew Lin & Liqi Wang, 2014. "A study of singular spectrum analysis with global optimization techniques," Journal of Global Optimization, Springer, vol. 60(3), pages 551-574, November.
    4. Stephen McKnight & Alexander Mihailov & Kerry Patterson & Fabio Rumler, 2014. "The Predictive Performance of Fundamental Inflation Concepts: An Application to the Euro Area and the United States," Economics & Management Discussion Papers em-dp2014-03, Henley Business School, Reading University.

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