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Testing Covariance Stationarity

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  • Zhijie Xiao
  • Luiz Renato Lima

Abstract

In this paper, we show that the widely used stationarity tests such as the Kwiatkowski Phillips, Schmidt, and Shin (KPSS) test have power close to size in the presence of time-varying unconditional variance. We propose a new test as a complement of the existing tests. Monte Carlo experiments show that the proposed test possesses the following characteristics: (i) in the presence of unit root or a structural change in the mean, the proposed test is as powerful as the KPSS and other tests; (ii) in the presence of a changing variance, the traditional tests perform badly whereas the proposed test has high power comparing to the existing tests; (iii) the proposed test has the same size as traditional stationarity tests under the null hypothesis of stationarity. An application to daily observations of return on U.S. Dollar/Euro exchange rate reveals the existence of instability in the unconditional variance when the entire sample is considered, but stability is found in subsamples.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 26 (2007)
Issue (Month): 6 ()
Pages: 643-667

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Handle: RePEc:taf:emetrv:v:26:y:2007:i:6:p:643-667

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Related research

Keywords: Asymptotic theory; KPSS; Stationarity testing; Time-varying variance;

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References

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Cited by:
  1. Kapetanios, George, 2009. "Testing for strict stationarity in financial variables," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2346-2362, December.
  2. George Kapetanios, 2007. "Testing for Strict Stationarity," Working Papers 602, Queen Mary, University of London, School of Economics and Finance.

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