On detecting and estimating a major level or slope change in general exponential smoothing

✍ Scribed by Duk B. Jun

• Publisher: John Wiley and Sons
• Year: 1989
• Tongue: English
• Weight: 517 KB
• Volume: 8
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/for.3980080105

✦ Synopsis

Exponential smoothing methods do not adapt well to a major level or slope change. In this paper, Bayesian statistical theory is applied to the dynamic linear model, altered by inclusion of dummy variables, and statistics are derived to detect such changes and to estimate both the change-point and the size. The paper also gives test statistics for such problems related to exponential smoothing. The statistics are simple functions of exponentially weighted moving averages of the forecast errors, using the same discount factor used in the exponential smoothing. Gardner has derived an approximate test statistic to detect a mean change in the constant mean model. When the present results are applied to this model they give the exact statistic. KEY WORDS Exponential smoothing Kalman filter Dynamic linear model Structural change Change-detection Change-point