Australian and New Zealand Journal of Statistics (2000), 42(4), 463-477.
Richard Fraccaro1,2, Rob J Hyndman1 and Alan Veevers2
- Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia.
- CSIRO Mathematical and Information Sciences, Clayton VIC 3168, Australia.
Abstract: This paper considers residuals for time series regression. Despite much literature on visual diagnostics for uncorrelated data, there is little on the autocorrelated case. In order to examine various aspects of the fitted time series regression model, three residuals are considered. The fitted regression model can be checked using orthogonal residuals; the time series error model can be analysed using marginal residuals; and the white noise error component can be tested using conditional residuals. When used together, these residuals allow identification of outliers, model mis-specification and mean shifts. Due to the sensitivity of conditional residuals to model mis-specification, it is suggested that the orthogonal and marginal residuals be examined first.
Keywords: autocorrelation; conditional residuals; generalised least squares; marginal residuals; mean shifts; model mis-specification; model transformation; orthogonal residuals; residual diagnostics; residual plots; time series regression.