Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties
AbstractMany unit root tests have distorted sizes when the root of the error process is close to the unit circle. This paper analyses the properties of the Phillips-Perron tests and some of their variants in the problematic parameter space. We use local asymptotic analyses to explain why the Phillips-Perron tests suffer from severe size distortions regardless of the choice of the spectral density estimator but that the modified statistics show dramatic improvements in size when used in conjunction with a particular formulation of an autoregressive spectral density estimator. We explain why kernel based spectral density estimators aggravate the size problem in the Phillips-Perron tests and yield no size improvement to the modified statistics. The local asymptotic power of the modified statistics are also evaluated. These modified statistics are recommended as being useful in empirical work since they are free of. the size problems which have plagued many unit root tests, and they retain respectable power. Copyright 1996 by The Review of Economic Studies Limited.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Review of Economic Studies.
Volume (Year): 63 (1996)
Issue (Month): 3 (July)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0034-6527
Other versions of this item:
- Tom Doan, . "PERRONNGMTESTS: RATS procedure to compute various Perron-Ng "M" unit root tests," Statistical Software Components RTS00155, Boston College Department of Economics.
- Perron, P. & Ng, S., 1994. "Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties," Cahiers de recherche 9427, Universite de Montreal, Departement de sciences economiques.
- Perron, P. & Ng, S., 1994. "Useful Modifications to Some Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties," Cahiers de recherche 9427, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
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