Documents 2012/71

A history analysis study involving three suggested outlier algorithms

Automatic outlier handling and model selection in seasonal adjustment

When using X-12-ARIMA for seasonal adjustment, several modeling decisions have to be taken. This can be viewed as a question of balancing the requirement of optimal seasonal adjustment at each time point against the requirement of minimal revisions.

The first part of this paper presents the most important results from a history analysis of 52 Norwegian economic time series (Langsrud, 2011). It is illustrated how revisions is affected by two automatic ARIMA model selection methods (automdl and pickmdl). Furthermore, it is shown that straightforward re-identification of outliers (the concurrent method) leads to big revisions. From this knowledge the second part of the paper considers the problem of automatically dealing with outliers (Langsrud, 2012). How should potential outliers be handled before the final decision is made? Three algorithms are suggested which can be named as “jump in and out”, “jump in” and “jump out”. It is demonstrated how revisions and out-of-sample forecasts (quality of model) are affected by using the algorithms. The results are compared to the concurrent method. The results indicate, however, that the best improvements are obtained by increasing the outlier detection limit. The analyses were made by running X-12-ARIMA via the R programming language.

About the publication

Title

Automatic outlier handling and model selection in seasonal adjustment. A history analysis study involving three suggested outlier algorithms

Author

Øyvind Langsrud

Series and number

Documents 2012/71

Publisher

Statistics Norway

Topic

Methods and documentation

ISBN (online)

978-82-537-8560-8

ISBN (printed)

978-82-537-8559-2

Number of pages

15

Language

English

About Documents

Documentation, descriptions of methods, models and standards are published in the series Documents.

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