dc.creatorPaiva, Denise de Assis
dc.creatorSáfadi, Thelma
dc.date2022-04-07T20:24:37Z
dc.date2022-04-07T20:24:37Z
dc.date2021
dc.date.accessioned2023-09-28T20:02:56Z
dc.date.available2023-09-28T20:02:56Z
dc.identifierPAIVA, D. de A.; SÁFADI, T. Study of tests for trend in time series. Brazilian Journal of Biometrics, [S. l.], v. 39, n. 2, p. 311-333, 2021. DOI: 10.28951/rbb.v39i2.471.
dc.identifierhttp://repositorio.ufla.br/jspui/handle/1/49708
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9043216
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.languageen_US
dc.publisherUniversidade Federal de Lavras
dc.rightsAttribution-NonCommercial 4.0 International
dc.rightsacesso aberto
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.sourceBrazilian Journal of Biometrics
dc.subjectEconomic series
dc.subjectTemperature series
dc.subjectStochastic and deterministic trend
dc.subjectSérie econômica
dc.subjectSéries de temperatura
dc.subjectTendência estocástica e determinística
dc.titleStudy of tests for trend in time series
dc.typeArtigo


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