Introduction to Seasonal Adjustment. Based on the: Australian Bureau of Statistics’ Information Paper: An Introductory Course on Time Series Analysis; Hungarian Central Statistical Office: Seasonal Adjustment Methods and Practices;
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Based on the:
Australian Bureau of Statistics’ Information Paper: An Introductory Course on Time Series Analysis;
Hungarian Central Statistical Office: Seasonal Adjustment Methods and Practices;
Bundesbank, Robert Kirchner: X-12 ARIMA Seasonal Adjustment of Economic Data Training Course
Statistics Development and Analysis Section
Seasonal adjustment has three main purposes:
The aim of seasonal adjustment is to eliminate seasonal and working day effects. Hence there are no seasonal and working-day effects in a perfectly seasonally adjusted series
In other words: seasonal adjustment transforms the world we live in into a world where no seasonal and working-day effects occur. In a seasonally adjusted world the temperature is exactly the same in winter as in the summer, there are no holidays, Christmas is abolished, people work every day in the week with the same intensity (no break over the weekend) etc.
Seasonal influences represent intra-year fluctuations in the series level, that are repeated more or less regularly year after year.
Trading day influences refer to the impact on the series, of the number and type of days in a particular month. A calendar month typically comprises four weeks (28 days) plus an extra one, two or three days. The activity for the month overall will be influenced by those extra days whenever the level of activity on the days of the week are different.
(The trading day influence is not as significant for quarterly series)
Components of timeseries
Additive Decomposition Model
Multiplicative Decomposition Model
The X12-ARIMA method is best described by the following flowchart, as presented by David Findley and by Deutsche Bundesbank respectively.