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Communications in Statistics - Theory and Methods Volume 53, 2024 - Issue 8
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Article

Unit root tests and their challenges

Seul Gee Kima Korea Institute of Oriental Medicine, Dajeon, KoreaView further author information
,
Cheolyong Parkb Department of Statistics, Keimyung University, Daegu, KoreaView further author information
,
Sun-Young Hwangc Department of Statistics, Sookmyung Women’s University, Seoul, KoreaView further author information
,
Jeongcheol Hab Department of Statistics, Keimyung University, Daegu, KoreaView further author information
,
Inho Parkd Department of Statistics, Pukyong University, Busan, KoreaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4216-915XView further author information
ORCID Icon &
Tae Yoon Kimb Department of Statistics, Keimyung University, Daegu, KoreaView further author information
Pages 2840-2847 | Received 22 Nov 2019, Accepted 17 Nov 2022, Published online: 26 Nov 2022

References

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  • Maddala, G. S, and I. M. Kim. 2003. Unit Roots, Cointegration and Structural Change. Oxford: Oxford University Press.
  • Müller, U. K. 2007. A theory of robust long run variance estimation. Journal of Econometrics 141 (2):1331–52. doi:10.1016/j.jeconom.2007.01.019.
  • Müller, U. K. 2008. The impossibility of consistent discrimination between I(0) and I(1) processes. Econometric Theory 24 (3):616–30. doi:10.1017/S0266466608080250.
  • Park, C., S. G. Kim, J. C. Park, J. Ha, and T. Y. Kim. 2018. Time series model identification in the frame of random walk model. Quantitative Bio-Science 37:1–17.
  • Park, C., S. Y. Hwang, J. Ha, and T. Y. Kim. 2019. Dickey-Fuller test for an extended MA model. Quantitative Bio-Science 38:1–21.
  • Phillips, P. C. B, and P. Perron. 1988. Testing for a unit root in time series regression. Biometrika 75 (2):335–46. doi:10.1093/biomet/75.2.335.

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