Abstract
An application of the empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we give the asymptotic distributions of the maximum empirical likelihood estimator and the empirical likelihood ratio statistics, respectively. It is shown that the empirical likelihood method enables us to make inferences on various important indices in a time series analysis. Furthermore, we give a numerical study and investigate a finite sample property.
Highlights
The empirical likelihood is one of the nonparametric methods for a statistical inference proposed by Owen 1, 2
The empirical likelihood method has been applied to various problems because of its good properties: generality of the nonparametric method and effectiveness of the likelihood method
Stationarity is the most fundamental assumption when we are engaged in a time series analysis, it is known that real time series data are generally nonstationary e.g., economics analysis
Summary
The empirical likelihood is one of the nonparametric methods for a statistical inference proposed by Owen 1, 2 It is used for constructing confidence regions for a mean, for a class of M-estimates that includes quantile, and for differentiable statistical functionals. Dahlhaus 11–13 proposed an important class of nonstationary processes, called locally stationary processes. They have so-called time-varying spectral densities whose spectral structures smoothly change in time. In this paper we extend the empirical likelihood method to non-Gaussian locally stationary processes with time-varying spectra. As an application of this method, we can estimate an extended autocorrelation for locally stationary processes.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
