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Seasonal Adjustment with the R Packages x12 and x12GUI

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  • Kowarik, Alexander
  • Meraner, Angelika
  • Templ, Matthias
  • Schopfhauser, Daniel

Abstract

The X-12-ARIMA seasonal adjustment program of the US Census Bureau extracts the different components (mainly: seasonal component, trend component, outlier component and irregular component) of a monthly or quarterly time series. It is the state-of-the- art technology for seasonal adjustment used in many statistical offices. It is possible to include a moving holiday effect, a trading day effect and user-defined regressors, and additionally incorporates automatic outlier detection. The procedure makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. The original output from X-12-ARIMA is somehow static and it is not always an easy task for users to extract the required information for further processing. The R package x12 provides wrapper functions and an abstraction layer for batch processing of X-12-ARIMA. It allows summarizing, modifying and storing the output from X-12-ARIMA within a well-defined class-oriented implementation. On top of the class-oriented (command line) implementation the graphical user interface allows access to the R package x12 without requiring too much R knowledge. Users can interactively select additive outliers, level shifts and temporary changes and see the impact immediately. The provision of the powerful X-12-ARIMA seasonal adjustment program available directly from within R, as well as of the new facilities for marking outliers, batch processing and change tracking, makes the package a potent and functional tool.

Suggested Citation

  • Kowarik, Alexander & Meraner, Angelika & Templ, Matthias & Schopfhauser, Daniel, 2014. "Seasonal Adjustment with the R Packages x12 and x12GUI," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i02).
    • Handle: RePEc:jss:jstsof:v:062:i02
    DOI: http://hdl.handle.net/10.18637/jss.v062.i02
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    References listed on IDEAS

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    1. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
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    Cited by:

    1. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.

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