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Time series forecasting using robust regression

✍ Scribed by Hans Levenbach

Publisher: John Wiley and Sons
Year: 1982
Tongue: English
Weight: 710 KB
Volume: 1
Category: Article
ISSN: 0277-6693
DOI: 10.1002/for.3980010304

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

The method of ordinary least squares (OLS) and generalizations of it have been the mainstay of most forecasting methodologies for many years. It is well‐known, however, that outliers or unusual values can have a large influence on least‐squares estimators. Users of automatic forecasting packages, in particular, need to be aware of the influence that outlying data values can have on statistical analyses and forecasting results. Robust methods are available to modify least‐squares procedures so that outliers have much less influence on the final estimates; yet these formal methods have not found their way into general forecasting procedures. This paper provides a case study in which classical least‐square‐estimation procedures are complemented with a robust alternative to enhance statistical fit criteria and improve forecasting performance. The study suggests that much can be gained in understanding the nature of outliers and their influence on forecasting performance by performing a robust regression in addition to OLS.

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