EFFECTS OF A SINGLE OUTLIER ON ARMA IDENTIFICATION

Fox (1972), Box and Tiao (1975), and Abraham and Box (1979) have proposed methods for detecting outliers in time series whose ARMA form is known (or identified). We show that the existence of a single aberrant observation, innovation, or intervention causes an ARMA model to be misidentified using unadjusted autocorrelation (acf) and partial autocorrelation estimates. The magnitude, location, type of outlier, and in some cases the ARMA’s parameters, affect the identification outcome. We use variance inflation, signal-to-noise ratios, and acf critical values to determine an ARMA model’s susceptibility to misidentification. Numerical and simulation examples suggest how to iteratively use the outlier detection methods in practice. © 1990, Taylor & Francis Group, LLC. All rights reserved.