# Choosing seasonal decomposition method

Seasonal adjustment is a crucial step preprocessing the data for further research. Researcher however has a number of options for trend-cycle-seasonal decomposition. The most common (judging by the number of citations in empirical literature) rival seasonal decomposition methods are X-11(12)-ARIMA, Tramo/Seats (both implemented in Demetra+) and $R$‘s stl. Seeking to avoid random choice between the above-mentioned decomposition techniques (or other simple methods like seasonal dummy variables) I would like to know a basic strategy that leads to choosing seasonal decomposition method effectively.

Several important subquestions (links to a discussion are welcome too) could be:

1. What are the similarities and differences, strong and weak points of the methods? Are there any special cases when one method is more preferable than the others?
2. Could you provide general guides to what is inside the black-box of different decomposition methods?
3. Are there special tricks choosing the parameters for the methods (I am not always satisfied with the defaults, stl for example has many parameters to deal with, sometimes I feel I just don’t know how to choose these ones in a right way).
4. Is it possible to suggest some (statistical) criteria that the time series is seasonally adjusted efficiently (correlogram analysis, spectral density? small sample size criteria? robustness?).