A comparison between Tau-d and the procedure TRAMO-SEATS is also included.

Perron and Rodríguez (2003) claimed that their procedure to detect for additive outliers (Tau-d) is powerful even when we have departures from the unit root case. In this note, we use Monte-Carlo simulations to show that Tau-d is powerful when we have ARFIMA(p; d; q) errors. Using simulations, we calculate the expected number of additive outliers found in this context and the number of times that the approach Tau-d identi…es the true location of the additive outliers. The results indicate that the power of the procedure Tau-d depends of the size of the additive outliers. When we have a DGP with big sized additive outliers the percentage of time that Tau-d detects correctly the location of the additive outliers is 100.0%.