Detection of additive outliers in seasonal time series
The detection and location of additive outliers in integrated variables has attracted much attention recently because such outliers tend to affect unit root inference among other things. Most of these procedures have been developed for non-seasonal processes. However, the presence of seasonality in the form of seasonally varying means and variances affect the properties of outlier detection procedures, and hence appropriate adjustments of existing methods are needed for seasonal data. In this paper we suggest modifications of tests proposed by Shin et al. (1996) and Perron and Rodriguez (2003) to deal with data sampled at a seasonal frequency and the size and power properties are discussed. We also show that the presence of periodic heteroscedasticity will inflate the size of the tests and hence will tend to identify an excessive number of outliers. A modified Perron-Rodriguez test which allows periodically varying variances is suggested and it is shown to have excellent properties in terms of both power and size.
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- Pierre Perron & Gabriel RodrÌguez, 2003.
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- Haldrup, Niels & Montanes, Antonio & Sanso, Andreu, 2005. "Measurement errors and outliers in seasonal unit root testing," Journal of Econometrics, Elsevier, vol. 127(1), pages 103-128, July.
- Niels Haldrup & Antonio Montanés & Andreu Sanso, "undated". "Measurement Errors and Outliers in Seasonal Unit Root Testing," Economics Working Papers 2000-8, Department of Economics and Business Economics, Aarhus University.
- Haldrup, Niels Prof. & Montanes, Antonio & SansÃ³, Andreu, 2000. "Measurement Errors and Outliers in Seasonal Unit Root Testing," University of California at San Diego, Economics Working Paper Series qt0gw7q9hk, Department of Economics, UC San Diego.
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