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A statistical adjustment that removes recurring seasonal patterns to make periods easier to compare.
Save conceptSeasonal adjustment removes estimated recurring patterns linked to the time of year from a statistical series. Retail activity often changes around holidays; agricultural work follows seasons. Without an adjustment, comparing December with January might mostly reveal a familiar calendar pattern rather than a new shift in the economy.
Statistical methods estimate recurring effects using the series’ past behaviour and relevant calendar information. The adjusted data help comparisons between adjacent months or quarters. They do not erase all unusual events or turn estimates into certainty. New observations can change the estimated seasonal pattern, so previously adjusted figures may be revised.
Suppose a business normally sells 100 units in an ordinary month and 150 in December. This December it sells 145. Sales are higher than November in raw numbers, yet lower than the usual December pattern. A seasonal analysis can help reveal that relative weakness. This simplified scenario illustrates the logic; official agencies estimate factors from much more information.
Seasonal adjustment is separate from adjustment. A series can be seasonally adjusted while still being , or be both real and seasonally adjusted. Year-on-year comparisons often reduce some recurring effects but have their own limitations. When comparing releases, use consistent adjustment labels and check whether a holiday or reporting-calendar change affected the estimates.