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TESTING TIME-SERIES STATIONARITY AGAINST AN ALTERNATIVE WHOSE MEAN IS PERIODIC

Published online by Cambridge University Press:  29 June 2001

Melvin A. Hinich  and
Phillip Wild
Melvin A. Hinich
Affiliation:
University of Texas at Austin
Phillip Wild
Affiliation:
University of Manchester
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Abstract

We develop a test of the null hypothesis that an observed time series is a realization of a strictly stationary random process. Our test is based on the result that the kth value of the discrete Fourier transform of a sample frame has a zero mean under the null hypothesis. The test that we develop will have considerable power against an important form of nonstationarity hitherto not considered in the mainstream econometric time-series literature, that is, where the mean of a time series is periodic with random variation in its periodic structure. The size and power properties of the test are investigated and its applicability to real-world problems is demonstrated by application to three economic data sets.

Keywords

Autoregressive (AR) ProcessDiscrete Fourier TransformRandomly Modulated Periodic ProcessSeasonalityStationarity
Type
Research Article
Information
Macroeconomic Dynamics , Volume 5 , Issue 3 , June 2001 , pp. 380 - 412
Copyright
© 2001 Cambridge University Press

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