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

Gabriel Rodríguez and Dionisio Ramirez
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Dionisio Ramirez: Universidad Castilla La Mancha

No 2013-355, Documentos de Trabajo / Working Papers from Departamento de Economía - Pontificia Universidad Católica del Perú

Abstract: 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%.

Keywords: Additive Outliers; ARFIMA Errors; Detection of Additive Out-liers. (search for similar items in EconPapers)
JEL-codes: C2 C3 C5 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-ets
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