Update On COVID-19 And Seasonal Adjustment – Methods Paper
The COVID-19 lockdown and restrictions have caused abrupt changes to actual values for many time series.
Our standard seasonal adjustment method (X13 ARIMA SEATS), was not able to handle the extreme movements in data points resulting from COVID-19-related disruptions in activity. X13 ARIMA allocated too much of the unusual value to the seasonal and trend components instead of the irregular component.
Untreated, this would have significantly altered the seasonal factors and caused large and undesirable revisions to the seasonally adjusted time series. As the COVID-19 disruption in the June 2020 quarter was an abrupt shock, it should be reflected in the irregular component and not affect historical seasonal patterns; allowing this would be incorrect and misleading.
To remedy this, we have identified and treated unusual data points for affected time series using additive outliers so that they were attributed to the irregular component.
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