Abstract
Seasonal and non-seasonal data are frequently observed with noise. For instance, the time series can have irregular abrupt changes and interruptions following as a result of additive or temporary change outliers caused by external circumstances. Equally, the time series can have measurement errors. In this paper we analyse the above types of data irregularities on the behavior of seasonal unit root tests. Outliers and measurement errors can seriously affect seasonal unit root inference and it is shown how the distortion of the tests will depend upon the frequency, magnitude, and persistence of the outliers as well as on the signal to noise ratio associated with measurement errors. Some solutions to the implied inference problems are suggested and shown to work in practice.
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