Performance of the ADF test in stationary series within structural breaks

Authors

DOI:

https://doi.org/10.5902/2179460X75150

Keywords:

ADF test, Time series, Structural breaks, Level shift

Abstract

The study of time series has been developed constantly, given the large volume of observed and measured data over the years. An important characteristic of time series is stationarity, which is mostly analyzed by unit root tests. It is a consensus in the literature that structural breaks, when present in the data series, can bias the result of the Augmented Dickey Fuller Test (ADF), the best known and most widely used method of stationarity investigation. So far, however, there is no consensus regarding the intensity that structural breaks can affect the power of the ADF Test, making the decision about using it difficult and possibly leading researchers to errors under those changes. Thus, this article analyzed the influence of level shift (LS) structural breaks in the stationarity analysis in annual time series using the ADF test through the rejection proportion of the null hypothesis. It was observed that this procedure tends to reject the null hypothesis in the presence of structural breaks in a possible confusion with the presence of a unit root. Furthermore, it was noted that, as the initial perturbation ω, increased, the power of the test was rapidly reduced, mainly with level change breaks imputed in positions closer to the origin of the data series.

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Author Biographies

Mariane Coelho Amaral, Universidade Federal do Rio Grande

Engenheira eletricista, Engenheira de Segurança do Trabalho e Licenciada em Matemática. Mestre em Modelagem computacional pela Universidade Federal do Rio Grande (FURG) e doutoranda em Modelagem Computacional. Possui especialização em Saúde e Segurança do Trabalho e em Engenharia de Segurança do Trabalho. Estudou Engenharia Elétrica no IFSul campus Pelotas e Graduação sanduíche na Universidade do Estado de New York. Tem como temas de interesse e estudo: simulações computacionais aplicadas às engenharias, análise multivariada de dados, testes de hipótese, testes de estacionariedade, inferência estatística e análises de séries temporais através de simulações computacionais. 

Anderson Silveira, Universidade Federal do Rio Grande

PhD in progress by the Graduate Program in Computational Modeling at the Federal University of Rio Grande. Master's degree from the Graduate Program in Computational Modeling from the Federal University of Rio Grande. Graduated in Electrical Engineering from Instituto Federal Sul-Rio-Grandense. Currently Professor at IFRS - Campus Rio Grande.

Viviane Leite Dias de Mattos, Universidade Federal do Rio Grande

Bachelor's degree in Civil Engineering from the Catholic University of Pelotas, Master's degree and Ph.D. in Production Engineering from the Federal University of Santa Catarina. Adjunct Professor at the Federal University of Rio Grande. Experience in Production Engineering, with emphasis on Quality Control Assurance, working mainly on the following topics: time series, quality, probability and statistics, process control and experiment design.

 

Andrea Cristina Konrath, Universidade Federal de Santa Catarina

Bachelor's degree in Applied and Computational Mathematics from the University of Santa Cruz do Sul, Master's degree in Production Engineering and Ph.D. in Mechanical Engineering from the Federal University of Santa Catarina. Lecturer in the area of ​​Statistics at the University of Vale do Itajaí (UNIVALI), from March 2007 to January 2009, and at the Institute of Mathematics, Statistics and Physics of the Federal University of Rio Grande (FURG), from February from 2009 to July 2011, also in the area of ​​Statistics. Since August 2011, she has been an assistant professor at the Federal University of Santa Catarina (UFSC).

Luiz Ricardo Nakamura, Federal University of Lavras

Bachelor's degree in Statistics from the São Paulo State University "Júlio de Mesquita Filho", Master's degree in Science (Statistics and Agronomic Experimentation) from the University of São Paulo and PhD in Science (Statistics and Agronomic Experimentation) from the University of São Paulo, with a period at London Metropolitan University. Adjunct Professor at the Department of Statistics at the Federal University of Lavras.

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Published

2023-12-01

How to Cite

Amaral, M. C., Silveira, A., Mattos, V. L. D. de, Konrath, A. C., & Nakamura, L. R. (2023). Performance of the ADF test in stationary series within structural breaks. Ciência E Natura, 45(esp. 3), e75150. https://doi.org/10.5902/2179460X75150

Issue

Vol. 45 No. esp. 3 (2023): ENMC/ECTM/MCSul/SEMENGO

Section

Special Edition

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