In this paper we investigate the use of the joint estimation (JE) outlier detection method as a statistical process control method for short-run autocorrelated data. Because JE is able to differentiate between four different outlier (out-of-control observation) types, performance is reported with respect to its ability to locate the out-of-control observation and identify the associated type. This is of particular interest to practitioners because the four different types may indicate different problems in the process. The results show that JE performs better for AR(1) models when the out-of-control observation is the last observation than for MA(1) models. However, JE is better able to distinguish between the four outlier types for MA(1) models than for AR(1) models. An example using real data is also provided.
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