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Testing for a change in persistence in the presence of non-stationary volatility

Listed author(s):
  • Cavaliere, Giuseppe
  • Taylor, A.M. Robert

In this paper we consider tests for the null of (trend-) stationarity against the alternative of a change in persistence at some (known or unknown) point in the observed sample, either from I(0) to I(1) behaviour or vice versa, of, inter alia, [Kim, J., 2000. Detection of change in persistence of a linear time series. Journal of Econometrics 95, 97-116]. We show that in circumstances where the innovation process displays non-stationary unconditional volatility of a very general form, which includes single and multiple volatility breaks as special cases, the ratio-based statistics used to test for persistence change do not have pivotal limiting null distributions. Numerical evidence suggests that this can cause severe over-sizing in the tests. In practice it may therefore be hard to discriminate between persistence change processes and processes with constant persistence but which display time-varying unconditional volatility. We solve the identified inference problem by proposing wild bootstrap-based implementations of the tests. Monte Carlo evidence suggests that the bootstrap tests perform well in finite samples. An empirical illustration using USÂ price inflation data is provided.

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File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(08)00126-7
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 147 (2008)
Issue (Month): 1 (November)
Pages: 84-98

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Handle: RePEc:eee:econom:v:147:y:2008:i:1:p:84-98
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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