1. bookVolume 37 (2021): Issue 2 (June 2021)
    Special Issue on New Techniques and Technologies for Statistics
Journal Details
License
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Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
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English
access type Open Access

A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models

Published Online: 22 Jun 2021
Page range: 367 - 394
Received: 01 May 2019
Accepted: 01 Sep 2020
Journal Details
License
Format
Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English
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

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