Testing for Seasonal Unit Roots by Frequency Domain Regression

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Testing for Seasonal Unit Roots by Frequency Domain Regression. / Ercolani, Joanne; Chambers, Marcus; Taylor, Robert.

In: Journal of Econometrics, 2013.

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@article{87b966e4cf5b479b988965c25b763700,
title = "Testing for Seasonal Unit Roots by Frequency Domain Regression",
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. Moreover, our analysis allows for (possibly innite order) moving average behaviour in the shocks, while extant large sample results pertaining to the Hylleberg et al. (1990) type tests are based on the assumption of a finite autoregression. 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, frequency domain regression, spectral density estimator",
author = "Joanne Ercolani and Marcus Chambers and Robert Taylor",
year = "2013",
doi = "10.1016/j.jeconom.2013.08.025",
language = "English",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Testing for Seasonal Unit Roots by Frequency Domain Regression

AU - Ercolani, Joanne

AU - Chambers, Marcus

AU - Taylor, Robert

PY - 2013

Y1 - 2013

N2 - 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. Moreover, our analysis allows for (possibly innite order) moving average behaviour in the shocks, while extant large sample results pertaining to the Hylleberg et al. (1990) type tests are based on the assumption of a finite autoregression. 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.

AB - 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. Moreover, our analysis allows for (possibly innite order) moving average behaviour in the shocks, while extant large sample results pertaining to the Hylleberg et al. (1990) type tests are based on the assumption of a finite autoregression. 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.

KW - Seasonal unit root tests

KW - frequency domain regression

KW - spectral density estimator

U2 - 10.1016/j.jeconom.2013.08.025

DO - 10.1016/j.jeconom.2013.08.025

M3 - Article

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

ER -