In this paper we extend the large-sample results provided for the augmented Dickey–Fuller test by Said and Dickey (<xref>1984</xref>, <italic>Biometrika</italic> 71, 599–607) and Chang and Park (<xref>2002</xref>, <italic>Econometric Reviews</italic> 21, 431–447) to the case of the augmented seasonal unit root tests of Hylleberg, Engle, Granger, and Yoo (<xref>1990</xref>, <italic>Journal of Econometrics</italic> 44, 215–238), inter alia. Our analysis is performed under the same conditions on the innovations as in Chang and Park (<xref>2002</xref>), thereby allowing for general linear processes driven by (possibly conditionally heteroskedastic) martingale difference innovations. We show that the limiting null distributions of the <italic>t</italic>-statistics for unit roots at the zero and Nyquist frequencies and joint <italic>F</italic>-type statistics are pivotal, whereas those of the <italic>t</italic>-statistics at the harmonic seasonal frequencies depend on nuisance parameters that derive from the lag parameters characterizing the linear process. Moreover, the rates on the lag truncation required for these results to hold are shown to coincide with the corresponding rates given in Chang and Park (<xref>2002</xref>); in particular, an <italic>o</italic>(<italic>T</italic> <sup>1/2</sup>) rate is shown to be sufficient.
