What is seasonal adjustment?

Seasonal adjustment refers to a statistical technique that tries to quantify and remove the influences of predictable seasonal patterns to reveal nonseasonal changes in data that would otherwise be overshadowed by the seasonal differences. Seasonal adjustments provide a clearer view of how the data evolves in the absence of seasonal influences.

Examples

Seasonal adjustments are intended to smooth out aberrances in certain types of data. That is, seasonally adjusted data are useful when comparing several months of data and the underlying data is subject to seasonal influences. For instance, as employment and unemployment varies strongly throughout the year, the monthly employment and unemployment numbers reported in the news are seasonally adjusted data. Throughout a year, the size of the labor force, the levels of employment and unemployment, and other measures of labor market activity experience fluctuations due to seasonal events including changes in weather, seasons, harvests, major holidays, and school schedules. Because these seasonal events follow a more or less regular pattern each year, their influence on statistical trends can be eliminated by seasonally adjusting the statistics from month to month. These seasonal adjustments make it easier to observe the cyclical, underlying trend, and other nonseasonal movements in the series.

Another example includes the sales of running shoes . Shoes bought in the summer exceeds the amount bought in the winter. A seasonal adjustment is therefore made to obtain a clear picture of the general trend.

Seasonal Adjust Data Yourself

Every interested person can easily seasonally adjust its own data. There exists various programs to seasonal adjust data. However, one easy and convenient alternative is seasonally adjustment in R. R is a free and widely used programming language with a large and active community. There exists many reasons why one should learn R. The following tutorial provides a manual that shows how to set-up and conduct seasonally adjustment in R.