Deletion diagnostics for transformations of time series

Journal article

Deletion diagnostics are derived for the effect of individual observations on the estimated transformation of a time series. The paper uses the modified power transformation of Box and Cox to provide a parametric family of transformations. Inference about the transformation parameter is made through regression on a constructed variable. The effect of deletion of observations on residuals and on the estimate of the regression parameter are obtained. Index plots of the diagnostic quantities are shown to be highly informative. Structural time series modelling is used, so that the results readily extend to inference about regression on other explanatory variables.

Authors: Atkinson, A; Shephard, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: John Wiley & Sons, Ltd.
• Journal: Journal of forecasting
• Volume: 15
• Issue: 1
• Pages: 1-17
• Publication date: 1996-01-01
• DOI: 10.1002/(SICI)1099-131X(199601)15:1<1::AID-FOR601>3.0.CO;2-6
• ISSN: 0277-6693
• Language: English
• Keywords: power transformation; gas usage; structural time series models; Box and Cox; Kalman filter; deletion diagnostics
• Subjects: Economics