UNIT ROOT TESTS IN THE PRESENCE OF MULTIPLE BREAKS IN VARIANCE

Soo-Bin Jeong, Bong-Hwan Kim, Tae-Hwan Kim () and Hyung-Ho Moon
Additional contact information
Soo-Bin Jeong: Yonsei University
Bong-Hwan Kim: University of California, San Diego
Hyung-Ho Moon: University of California, San Diego

No 2014rwp-70, Working papers from Yonsei University, Yonsei Economics Research Institute

Abstract: Spurious rejections of the standard Dickey-Fuller (DF) test caused by a single variance break have been reported and some solutions to correct the problem have been proposed in the literature. Kim et al. (2002) put forward a correctly-sized unit root test robust to a single variance break, called the KLN test. However, there can be more than one break in variance in time-series data as documented in Zhou and Perron (2008), so allowing only one break can be too restrictive. In this paper, we show that multiple breaks in variance can generate spurious rejections not only by the standard DF test but also by the KLN test. We then propose a bootstrap-based unit root test that is correctly-sized in the presence of multiple breaks in variance. Simulation experiments demonstrate that the proposed test performs well regardless of the number of breaks and the location of the breaks in innovation variance.

Keywords: Dickey-Fuller test; variance break; wild bootstrap. (search for similar items in EconPapers)
JEL-codes: C12 C15 (search for similar items in EconPapers)
Pages: 23pages
Date: 2014-11
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: UNIT ROOT TESTS IN THE PRESENCE OF MULTIPLE BREAKS IN VARIANCE (2017)
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