Redesigning the classical automatic selection of X-11 seasonal filters | 국외연구자료

KDI 경제교육·정보센터

Economic Information and Education Center

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Redesigning the classical automatic selection of X-11 seasonal filters

Deutsche Bundesbank

2026.06.16

- How can we improve the quality of seasonally adjusted estimates obtained with up-to-date methods tailored to the complexities of modern daily and weekly economic data? This study revisits the classical data-driven selection of seasonal filters in the popular X-11 method and proposes a generic redesign for a recent X-11 modification. A crucial step is the proper treatment of superimposed seasonal dynamics often displayed in daily economic time series. By addressing this and other challenges, the present research offers a robust strategy for improving seasonal adjustment adequacy for real-time economic data with potentially complex seasonality.

- Seasonal adjustment is a cornerstone of economic data analysis, enabling policymakers and researchers to identify more clearly underlying long-term trends and sudden short-term changes by removing recurring sub-annual patterns. The X-11 method for monthly and quarterly data, introduced in 1967, has been a foundational tool in this domain, evolving through various extensions such as X-12-ARIMA and X-13ARIMA-SEATS. Its most recent modification, which is available in the JDemetra+ time series software for official statistics and described in Webel and Smyk (2024), enables seasonal adjustment of time series with any seasonal periodicity but does not inherit the classical method’s popular mechanism for a data-driven selection of seasonal filters. To fill this gap, this study proposes a generic redesign of this legacy mechanism. Real-time macroeconomic data for Germany, covering quarterly gross domestic product (GDP), monthly industrial production, and daily realised electricity consumption, are used to illustrate one specific redesigned selection rule.

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