Size and Power of Tests for Stationarity in Highly Autocorrelated Time Series

Ulrich K. Müller ()

University of St. Gallen Department of Economics working paper series 2002 from Department of Economics, University of St. Gallen

Abstract: Tests for stationarity are routinely applied to highly persistent time series. Following Kwiatkowski, Phillips, Schmidt and Shin (1992), standard stationarity employs a rescaling by an estimator of the long-run variance of the (potentially) stationary series. This paper analytically investigates the size and power properties of such tests when the series are strongly autocorrelated in a local-to-unity asymptotic framework. It is shown that the behavior of the tests strongly depends on the long-run variance estimator employed, but is in general highly undesirable. Either the tests fail to control for size even for strongly mean reverting series, or they are inconsistent against an integrated process and discriminate only poorly between stationary and integrated processes compared to optimal statistics.

Keywords: Tests for stationarity; local-to-unity asymptotics; long-run variance estimation; mean reversion (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2002-11
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://ux-tauri.unisg.ch/RePEc/usg/dp2002/dp0226mueller_ganz.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2002:2002-26

Access Statistics for this paper

More papers in University of St. Gallen Department of Economics working paper series 2002 from Department of Economics, University of St. Gallen Contact information at EDIRC.
Bibliographic data for series maintained by Joerg Baumberger ( this e-mail address is bad, please contact ).