IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

WIFO's New Seasonal Adjustment Procedure

Listed author(s):
  • Michael Wüger


Economic time series are submitted to seasonal adjustment procedures in order to facilitate the analysis of the data. Originally, deterministic approaches were adopted for the non-observable components like trend, seasonal factors, etc. Later on, they were replaced by the calculation of moving averages. Best known are "Census X–11" for seasonal and "HP filter" for trend adjustment. The advantage of such ad-hoc filters lies in their easy applicability, their disadvantage is that "they take a look at the world from a particular perspective", with possibly far-reaching negative consequences. Also, the lack of a statistical model detracts from a meaningful application of ad-hoc filters. In order to eliminate the problems attached to ad-hoc filters, new ways of estimating the non-observable components have been developed over the last 15 years. With the ARIMA model-based approaches, one first identifies a model of the observable time series and then derives from it models for the particular non-observable components (trend, season, irregular component) which are compatible with the global model. For the selection of an optimal seasonal adjustment method, both theoretical and empirical criteria should be applied. In a comprehensive study, Eurostat has examined five different seasonal adjustment procedures using more than 80 time series. TRAMO/SEATS – an ARIMA model based approach – proved to be the most sophisticated theoretical method and also performed best in most empirical tests. The TRAMO/SEATS program set allows the modeling of a time series via the combined estimation of ARIMA approaches and outlier as well as special effects, while also protecting against over- or underdifferentiation. It also allows a comparison between the components derived from the theoretical model and the empirical estimators, thereby facilitating the diagnosis. Furthermore, it offers a number of analytical tools which form the basis for statistically robust conclusions. Application of TRAMO/SEATS to time series of retail sales and consumption expenditure shows that it produces appropriate models for the explanation of these data. The outliers identified in these time series are readily explained by the expected effects of discretionary fiscal measures and changes in the statistical base. The theoretically obtained components correspond to a high degree to the empirically estimated ones. The series seasonally adjusted with TRAMO/SEATS exhibit markedly weaker variations in growth rates than those adjusted with X11, which makes interpretation of the data easier. The empirical record so far as well as test results of Eurostat suggest that this approach should be adopted for seasonal adjustment also in Austria.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
File Function: Abstract
Download Restriction: Payment required

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by WIFO in its journal WIFO-Monatsberichte.

Volume (Year): 68 (1995)
Issue (Month): 10 (October)
Pages: 625-635

in new window

Handle: RePEc:wfo:monber:y:1995:i:10:p:625-635
Contact details of provider: Postal:
Arsenal Object 20, A-1030 Wien

Phone: (+43 1) 798 26 01-0
Fax: (+43 1) 798 93 86
Web page:

More information through EDIRC

Order Information: Postal: Austrian Institute of Economic Research Publikationsverkauf und Abonnentenbetreuung Arsenal, Objekt 20 A-1030 Vienna/Austria

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:wfo:monber:y:1995:i:10:p:625-635. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ilse Schulz)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.