Bernoulli

  • 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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Abstract

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.

Article information

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

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

Permanent link to this document
https://projecteuclid.org/euclid.bj/1438777587

Digital Object Identifier
doi:10.3150/14-BEJ636

Mathematical Reviews number (MathSciNet)
MR3378460

Zentralblatt MATH identifier
1342.60071

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

Citation

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. https://projecteuclid.org/euclid.bj/1438777587


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