Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

Martelotte Marcela Cohen (), Souza Reinaldo Castro () and Silva Eduardo Antônio Barros Da ()
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Martelotte Marcela Cohen: Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro. Rua Marquês de São Vicente, 225 - Gávea, Rio de Janeiro, Brazil
Souza Reinaldo Castro: Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro. Rua Marquês de São Vicente, 225 - Gávea, Rio de Janeiro, Brazil
Silva Eduardo Antônio Barros Da: Electrical Engineering Program, Federal University of Rio de Janeiro. C.P. 68504, Rio de Janeiro, Brazil

Journal of Official Statistics, 2017, vol. 33, issue 1, 155-186

Abstract: Considering that many macroeconomic time series present changing seasonal behaviour, there is a need for filters that are robust to such changes. This article proposes a method to design seasonal filters that address this problem. The design was made in the frequency domain to estimate seasonal fluctuations that are spread around specific bands of frequencies. We assessed the generated filters by applying them to artificial data with known seasonal behaviour based on the ones of the real macroeconomic series, and we compared their performance with the one of X-13A-S. The results have shown that the designed filters have superior performance for series with pronounced moving seasonality, being a good alternative in these cases.

Keywords: Moving seasonality; Filter design; Frequency domain; Time series decomposition; X-13A-S (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:33:y:2017:i:1:p:155-186:n:9

DOI: 10.1515/jos-2017-0009

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