<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models"}

Methodology for seasonality diagnostics is extremely important for statistical agencies, because such tools are necessary for making decisions whether to...

A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models

Pubblicato online: 22 giu 2021

Pagine: 367 - 394

Ricevuto: 01 mag 2019

Accettato: 01 set 2020

DOI: https://doi.org/10.2478/jos-2021-0016

Parole chiave
Autoregressive estimator, seasonal adjustment, spectral peaks, visual significance

© 2021 Tucker McElroy, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Diagnostic for Seasonality Based Upon Polynomial Roots of ARMA Models

Pubblicato online: 22 giu 2021

Pagine: 367 - 394

Ricevuto: 01 mag 2019

Accettato: 01 set 2020

DOI: https://doi.org/10.2478/jos-2021-0016

Parole chiaveAutoregressive estimator, seasonal adjustment, spectral peaks, visual significance

© 2021 Tucker McElroy, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Autoregressive estimator, seasonal adjustment, spectral peaks, visual significance