Diagnostic tools for selecting the temporal resolution for seasonal adjustment

Official statistics increasingly make use of higher-frequency time series. But when users ultimately are interested in a seasonally adjusted temporal aggregate of these data, we have to decide whether to perform seasonal adjustment or aggregation first. Consequently, we must weigh up the benefits of richer informational content against the increased computational requirements and the challenges presented by using more volatile and outlier-prone data. We examine this trade-off on simulated and real-world time series using a battery of diagnostics including revision size, tests on residual seasonal and calendar effects and linkage with target variables using leading adjustment procedures: DSA, WSA, X-13-ARIMA, and TRAMOSEATS. We synthesise our findings into practical guidelines that help users choose the aggregation level that balances statistical quality and real-time usefulness.