Publication Date: August 1996
Revision Date: October 1997
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.
Chaos, kernel, nonlinear dynamics, nonparametric regression, semiparametric
JEL Classification Codes: C13, C14, C22
Published in Journal of Econometrics (July 1999), 91(1): 1-42 [DOI]