Study of tests for trend in time series

The time series methodology is an important tool when using data over time. The time series can be composed of the components trend (Tt), seasonality (St) and the random error (at). The aim of this study was to evaluate the tests used to analyze the trend component, which were: Pettitt, Run, Mann-Ke...

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Detalhes bibliográficos
Autores: Paiva, Denise de Assis, Sáfadi, Thelma
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:Brasil
Recursos:Universidade Federal de Lavras (UFLA)
Repositorio:Repositório Institucional da UFLA
Idioma:inglés
OAI Identifier:oai:repositorio.ufla.br:1/49708
Acesso em linha:https://repositorio.ufla.br/handle/1/49708
Access Level:acceso abierto
Palavra-chave:Economic series
Temperature series
Stochastic and deterministic trend
Série econômica
Séries de temperatura
Tendência estocástica e determinística
Descrição
Resumo:The time series methodology is an important tool when using data over time. The time series can be composed of the components trend (Tt), seasonality (St) and the random error (at). The aim of this study was to evaluate the tests used to analyze the trend component, which were: Pettitt, Run, Mann-Kendall, Cox-Stuart and the unit root tests (Dickey-Fuller, Dickey-Fuller Augmented and Zivot and Andrews), given that there is a discrepancy between the test results found in the literature. The four series analyzed were the maximum temperature in the Lavras city, MG, Brazil, the unemployment rate in the Metropolitan Region of S˜ao Paulo (RMSP), the Broad Consumer Price Index (IPCA) and the nominal Gross Domestic Product (GDP) of Brazil. It was found that the unit root tests showed similar results in relation to the presence of the stochastic trend for all series. Furthermore, the turning point of the Pettitt test diverged from all the structural breaks found through the Zivot and Andrews test, except for the GDP series. Therefore, it was found that the trend tests diverged, obtaining similar results only in relation to the unemployment series.