The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series
This paper derives the asymptotic distribution of a smoothing-based estimator of the Lyapunov exponent for a stochastic time series under two general scenarios. In the first case, we are able to establish root-T consistency and asymptotic normality, while in the second case, which is more relevant for chaotic processes, we are only able to establish asymptotic normality at a slower rate of convergence. We provide consistent confidence intervals for both cases. We apply our procedures to simulated data.
|Date of creation:||Oct 1997|
|Publication status:||Published in Journal of Econometrics (1996), 91: 1-42|
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|Order Information:|| Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA|
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