Testing for seasonal unit roots by frequency domain regression

Marcus Chambers, Joanne S. Ercolani and Robert Taylor

Journal of Econometrics, 2014, vol. 178, issue P2, 243-258

Abstract: This paper develops univariate seasonal unit root tests based on spectral regression estimators. An advantage of the frequency domain approach is that it enables serial correlation to be treated non-parametrically. We demonstrate that our proposed statistics have pivotal limiting distributions under both the null and near seasonally integrated alternatives when we allow for weak dependence in the driving shocks. This is in contrast to the popular seasonal unit root tests of, among others, Hylleberg et al. (1990) which treat serial correlation parametrically via lag augmentation of the test regression. Our analysis allows for (possibly infinite order) moving average behaviour in the shocks. The size and power properties of our proposed frequency domain regression-based tests are explored and compared for the case of quarterly data with those of the tests of Hylleberg et al. (1990) in simulation experiments.

Keywords: Seasonal unit root tests; Moving average; Frequency domain regression; Spectral density estimator; Brownian motion (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Working Paper: Testing for seasonal unit roots by frequency domain regression (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:p2:p:243-258

DOI: 10.1016/j.jeconom.2013.08.025

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