Seasonal adjustment is a very important step for economic data
preprocessing. Holiday adjustment is an inevitable step for the popular
seasonal adjustment methods which include X-12-ARIMA and TRAMO/SEATS.
Because different countries have different kinds of holidays, the
popular seasonal adjustment methods must be modified when they are used
in different countries. For China, the Spring Festival is a very
important and comparatively long holiday, and it occurs in January in
some years and in February in other years. On the base of the X-12-ARIMA
method, taking the Chinese Spring Festival for example, this paper
designs different kinds of moving holiday models with considering the
effects of holiday’s influence as well as spans on economic data. By
selecting different economic indicators and by using software Demetra
and EViews, this paper tests the performance of these different models.
In the end, the best adjustment models are derived based on the criteria
of outlier percentage reduction.