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Diagnostic tools for selecting the temporal resolution for seasonal adjustment
We propose a decision tree to guide the choice of temporal aggregation for seasonal adjustment. The process begins by defining the use case and eliminating unsuitable aggregation levels. For instance, if the goal is to use the series as a higher-frequency proxy for a quarterly target, quarterly aggregation is only relevant if the target series is significantly delayed. If the primary objective is to proxy a target, we recommend selecting the aggregation level that maximises alignment with the target, using out-of-sample prediction accuracy metrics when applicable.
If no target series exists or if other objectives are also important, we suggest considering the series‘ properties. We advise avoiding quarterly aggregation when calendar effects are critical, as identifying these effects at that frequency is often unreliable. For additional effects like cross-seasonal patterns, daily or higher frequencies are preferable, as they allow for more reliable estimation. Finally, we recommend evaluating the remaining temporal resolutions using diagnostics and metrics, summarising them via mean ranks, and selecting the option with the lowest rank.
If no target series exists or if other objectives are also important, we suggest considering the series‘ properties. We advise avoiding quarterly aggregation when calendar effects are critical, as identifying these effects at that frequency is often unreliable. For additional effects like cross-seasonal patterns, daily or higher frequencies are preferable, as they allow for more reliable estimation. Finally, we recommend evaluating the remaining temporal resolutions using diagnostics and metrics, summarising them via mean ranks, and selecting the option with the lowest rank.
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