Detection of outliers and level shifts in time series: An evaluation of two alternative procedures

A unified method to detect and handle innovational and additive outliers, and permanent and transient level changes has been presented by Tsay. Balke has found that the presence of level changes may lead to misidentification and loss of test-power, and suggests augmenting Tsay's procedure by conducting an additional disturbance search based on a white-noise model. While Tsay allows level changes to be either permanent or transient, Balke considers only the former type. Based on simulated series with transient level changes this paper investigates how Balke's white-noise model performs both when transient change is omitted from the model specification and when it is included. Our findings indicate that the alleged misidentification of permanent level changes may be influenced by the restrictions imposed by Balke. But when these restrictions are removed, Balke's procedure outperforms Tsay's in detecting changes in the data-generating process.