 A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA ModelsJournal of Official Statistics, 2021, vol. 37, issue 2, 367-394 Abstract: Methodology for seasonality diagnostics is extremely important for statistical agencies, because such tools are necessary for making decisions whether to seasonally adjust a given series, and whether such an adjustment is adequate. This methodology must be statistical, in order to furnish quantification of Type I and II errors, and also to provide understanding about the requisite assumptions. We connect the concept of seasonality to a mathematical definition regarding the oscillatory character of the moving average (MA) representation coefficients, and define a new seasonality diagnostic based on autoregressive (AR) roots. The diagnostic is able to assess different forms of seasonality: dynamic versus stable, of arbitrary seasonal periods, for both raw data and seasonally adjusted data. An extension of the AR diagnostic to an MA diagnostic allows for the detection of over-adjustment. Joint asymptotic results are provided for the diagnostics as they are applied to multiple seasonal frequencies, allowing for a global test of seasonality. We illustrate the method through simulation studies and several empirical examples. Keywords: Autoregressive estimator; seasonal adjustment; spectral peaks; visual significance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:37:y:2021:i:2:p:367-394:n:8 Access Statistics for this article Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson More articles in Journal of Official Statistics from Sciendo |
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