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

U of St. Gallen, Economics Discussion Paper No. 2002-26

26 Pages Posted: 5 Feb 2003

See all articles by Ulrich K. Müller

Ulrich K. Müller

Princeton University - Department of Economics

Date Written: November 2002

Abstract

Tests for stationarity are routinely applied to highly persistent time series. Following Kwiatkowski, Phillips, Schmidt and Shin (1992), standard stationarity tests employ 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

JEL Classification: C12, C22

Suggested Citation

Müller, Ulrich K., Size and Power of Tests for Stationarity in Highly Autocorrelated Time Series (November 2002). U of St. Gallen, Economics Discussion Paper No. 2002-26, Available at SSRN: https://ssrn.com/abstract=371980 or http://dx.doi.org/10.2139/ssrn.371980

Ulrich K. Müller (Contact Author)

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States
609-258-3216 (Phone)
609-258-4026 (Fax)

HOME PAGE: http://www.princeton.edu/~umueller

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