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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- 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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0005e, University of Ottawa, Department of Economics.
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"A Note on the Vogelsang Test for Additive Outliers,"
Economics Working Papers
2006-01, School of Economics and Management, University of Aarhus.
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Journal of Econometrics,
Elsevier, vol. 104(1), pages 91-117, August.
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"Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties,"
Cahiers de recherche
9427, Universite de Montreal, Departement de sciences economiques.
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