• Bernoulli
  • Volume 21, Number 4 (2015), 2093-2119.

An empirical likelihood approach for symmetric $\alpha$-stable processes

Fumiya Akashi, Yan Liu, and Masanobu Taniguchi

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Empirical likelihood approach is one of non-parametric statistical methods, which is applied to the hypothesis testing or construction of confidence regions for pivotal unknown quantities. This method has been applied to the case of independent identically distributed random variables and second order stationary processes. In recent years, we observe heavy-tailed data in many fields. To model such data suitably, we consider symmetric scalar and multivariate $\alpha$-stable linear processes generated by infinite variance innovation sequence. We use a Whittle likelihood type estimating function in the empirical likelihood ratio function and derive the asymptotic distribution of the empirical likelihood ratio statistic for $\alpha$-stable linear processes. With the empirical likelihood statistic approach, the theory of estimation and testing for second order stationary processes is nicely extended to heavy-tailed data analyses, not straightforward, and applicable to a lot of financial statistical analyses.

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Bernoulli, Volume 21, Number 4 (2015), 2093-2119.

Received: April 2013
Revised: April 2014
First available in Project Euclid: 5 August 2015

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confidence region empirical likelihood ratio heavy tail normalized power transfer function self-normalized periodogram symmetric $\alpha$-stable process Whittle likelihood


Akashi, Fumiya; Liu, Yan; Taniguchi, Masanobu. An empirical likelihood approach for symmetric $\alpha$-stable processes. Bernoulli 21 (2015), no. 4, 2093--2119. doi:10.3150/14-BEJ636.

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