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Seasonal Adjustment Pitfalls
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Pitfalls

For indicators with strong seasonal patterns, comparisons between consecutive periods (e.g. quarters) should not be done using non-seasonally adjusted series.

Again taking an example of the unemployment rate, a person looking at the time series of the non-seasonally adjusted unemployment rate may come to the conclusion that unemployment had worsened in June 2010 compared with March 2010, on the basis that the rate increased from 2.1% to 2.8%. It turned out that the unemployment rate was actually slightly lower in June 2010 compared with March 2010, after we remove the seasonal component from the time series.

Overall Unemployment Rate


Note: The seasonally adjusted unemployment figures are subject to annual revisions when the latest set of seasonal factors is updated, taking into account observations for the latest available year. The most up-to-date data can be accessed her​​e.

In analysing time series data especially those that display strong seasonal patterns, it would be more appropriate to consider the quarter-on-quarter or month-on-month changes based on the seasonally adjusted data. This allows for a more meaningful comparison of the time series trends rather than that based on the non-seasonally adjusted series, as changes in the unadjusted data over the quarter or over the month due to seasonal influences may mistakenly be taken as a change due to trend or cyclical factors.

In the absence of seasonally adjusted data for a time series, it would be more meaningful to study the changes in the time series compared with the same period of each year. The exception is for series which do not have identifiable seasonal patterns, or where the seasonal patterns are weak. 

For example, we want to study the trends in the unemployment rate for a group where seasonally adjusted data is not available. In this case, we should compare the changes between the same period across years (e.g. unemployment rate in June this year, compared with unemployment rate in June last year and/or in earlier years). This would allow us to make a better assessment of the trends in the data without it being distorted by seasonal patterns. Alternatively, for a comparison across years, we could use the annual average, which provides a measure of the indicator for the whole year.

Note also that seasonally adjusted data are not comparable with non-seasonally adjusted data. For example, one should not compare the seasonally adjusted resignation rate for the fourth quarter of 2011 with the non-seasonally adjusted resignation rate in the third quarter of 2011, and conclude that the resignation rate has increased, decreased or stayed the same.