A Consistent Nonparametric Test of Ergodicity for Time Series with Applications

Ian Domowitz and Mahmoud El-Gamal

Working papers from Wisconsin Madison - Social Systems

Abstract: We propose a set of algorithms for testing the ergodicity of empirical time series, without reliance on a specific parametric framework. It is shown that the resulting test asymptotically obtains the correct size for stationary and nonstationary processes, and maximal power against non-ergodic but stationary alternatives. The test will not reject in the presence of nonstationarity that does not lead to ergodic failure. The work is linked to recent research on reformulations of the concept of integrated processes of order zero, and we demonstrate the means to operationalize new concepts of "short memory" for economic time series. Limited Monte Carlo evidence is provided with respect to power against the non-stationary and non-ergodic alternative of unit root processes. The method is used to investigate debates over stability of monetary aggregates relative to GDP, and the mean reversion hypothesis with respect to high frequency data on exchange rates.

Keywords: TESTS; TIME SERIES (search for similar items in EconPapers)
JEL-codes: C30 C32 C52 (search for similar items in EconPapers)
Pages: 36 pages
Date: 1997
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: A consistent nonparametric test of ergodicity for time series with applications (2001)
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