Using the Correlation Exponent to Decide whether an Economic Series is Chaotic
AbstractWe consider two ways of distinguishing deterministic time-series from stochastic white noise; the Grassberger-Procaccia correlation exponent test and the Brock, Dechert, Scheinkman (or BDS) test. Using simulated data to test the power of these tests, the correlation exponent test can distinguish white noise from chaos. It cannot distinguish white noise from chaos mixed with a small amount of white noise. With i.i.d. as the null, the BDS correctly rejects the null when the data are deterministic chaos. Although the BDS test may also reject the null even when the data are stochastic, it may be useful in distinguishing between linear and nonlinear stochastic processes. Copyright 1992 by John Wiley & Sons, Ltd.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 7 (1992)
Issue (Month): S (Suppl. Dec.)
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
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- Mattarocci, Gianluca, 2006. "Market characteristics and chaos dynamics in stock markets: an international comparison," MPRA Paper 4296, University Library of Munich, Germany, revised Jun 2006.
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- Jose-Manuel Rey & Manuel Mor�n, 1999. "A Formalism for the Dimensional Analysis of Time Series," Computing in Economics and Finance 1999 1331, Society for Computational Economics.
- M. Shibley Sadique, 2011. "Testing for Neglected Nonlinearity in Weekly Foreign Exchange Rates," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 77-88, June.
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