2019
DOI: 10.1016/j.ecosta.2017.11.002
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Model order selection in periodic long memory models

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Cited by 9 publications
(5 citation statements)
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“…In many applications, the problem at hand will suggest a proper choice of the fre-quencies λ j to be considered in ( 14), and hence the model order k. In some applications, however, the choice of k might be less clear a priori, and an automatic selection procedure could be warranted. Leschinski and Sibbertsen (2019) provide such a procedure, which is based on sequential tests for the maximum of the spectral density of the iteratively Gegenbauer-filtered data and illustrated for hourly electricity loads. The latter type of HF data has also been analysed by Soares and Souza (2006) with the aid of periodic regressions that contain dummy variables for weekly seasonality and holiday variation and 2-factor Gegenbauer errors that capture trend behaviour and annual seasonality.…”
Section: Generalised Gegenbauer Processesmentioning
confidence: 99%
“…In many applications, the problem at hand will suggest a proper choice of the fre-quencies λ j to be considered in ( 14), and hence the model order k. In some applications, however, the choice of k might be less clear a priori, and an automatic selection procedure could be warranted. Leschinski and Sibbertsen (2019) provide such a procedure, which is based on sequential tests for the maximum of the spectral density of the iteratively Gegenbauer-filtered data and illustrated for hourly electricity loads. The latter type of HF data has also been analysed by Soares and Souza (2006) with the aid of periodic regressions that contain dummy variables for weekly seasonality and holiday variation and 2-factor Gegenbauer errors that capture trend behaviour and annual seasonality.…”
Section: Generalised Gegenbauer Processesmentioning
confidence: 99%
“…The periodogram is usually computed at the Fourier frequencies λ j = 2π j/T , for j = 1, ..., n and n = T/2 . A semiparametric test for seasonal long memory with period S is obtained by employing a modified version of the G * test that was suggested by Leschinski and Sibbertsen (2014) in the context of model selection in GARMA models. Their procedure tests for seasonal long memory using the test statistic…”
Section: Testing For Seasonal Long Memorymentioning
confidence: 99%
“…However, this has the disadvantage that a single large periodogram ordinate I(λ j ) has a significant impact on the spectral density estimate in the neighborhood of λ j . To avoid this effect, Leschinski and Sibbertsen (2014) adopt the logspline spectral density estimate originally proposed by Cogburn et al (1974), who showed that this estimator is asymptotically equivalent to a kernel spectral density estimate. A maximum likelihood version of this estimator based on regression splines was proposed by Kooperberg et al (1995).…”
Section: Testing For Seasonal Long Memorymentioning
confidence: 99%
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