Deseasonalizing is known as the process by which a specific variable or measurement is unlinked from a given period of time.
In the economic and mathematical field, seasonal adjustment is the action of ignoring or not taking into account the seasonal effect in a time series. In this way, said influence is not included when obtaining readings or conclusions from said data groups.
Another way of understanding this numerical phenomenon is under the name of seasonal adjustment.
Deseasonalize as a technical procedure
The creation of temporary or numerical series of a seasonally adjusted nature is in itself a process of high technical difficulty, which is why it is common to resort to computer programming and different specific mathematical procedures.
In a basic scheme, eliminating the seasonal component of a series means stripping it of its regular behavior at the same time.
This happens in a large part of the time series that are obtained and that obey the natural order or the behavior of the economic cycles themselves.
The mathematical basis when deseasonalizing
Although this is an algorithmically complex process, the basic mathematical idea behind the concept of seasonal adjustment is as follows.
It is necessary to differentiate between a moment X and another called X-1. In this way, statistically it is possible to alleviate or stabilize both the mean and the variance of the treated series.
If we had, for example, the production data of a factory between months, by seasonally adjusting, we eliminate the influence of the time of year or the climate in which they are found. Thus, we will compare its evolution in a constant and seasonally adjusted perspective.
Economic application of deseasonalize
This mathematical concept is especially important in what are known as calculations of econometric models. Through deseasonalization, it is possible to eliminate the variation of time to know the possible effects of a change or modification of a variable such as price, for example.
A very common application is when presenting data related to employment. Sometimes the institutions related to this matter present job creation data seasonally adjusted. This means that the results shown do not take into account phenomena such as high seasons (common to sectors such as tourism, for example) or changes in the weather season.
On the other hand, this mathematical practice also facilitates the prediction of various economic variables. An example of this is the observation of stock values regarding their price volatility.