Seasonal adjustment

Seasonal adjustment is a statistical method in the field of time series analysis.

In econometrics is assumed that a time series is composed of several components:

• Trend component
• Cyclical component
• Seasonal component

Overall, the decomposition of a time series is in the standard model. The seasonal component in this case represents a periodic signal whose occurrence and effect in the data is usually known. ( For example, the unemployment rate varies considerably within a year with the seasons ). Since this effect is well known and usually dominates the spectrum of the time series, this should be filtered using the seasonal adjustment to investigate hidden periodicities, the trend or the nature of the random process in the data closer.

Often moving averages are used for seasonal adjustment applied: Does the order of a moving average exactly the period of the seasonal effect, it will be eliminated by the averaging of the time series.

Many modern techniques such as X-12 -ARIMA or the Berlin method partly based on convolutions of moving averages.

The problem of seasonal adjustment plays an important role, especially in official statistics. This usually statistical software is used. For example, the Federal Statistical Office, the program BV4.1 has developed. It is based on the current version 4.1 of the so-called Berlin procedure.

• Time series analysis