AN EMPIRICAL LIKELIHOOD APPROACH FOR NON-GAUSSIAN VECTOR STATIONARY PROCESSES AND ITS APPLICATION TO MINIMUM CONTRAST ESTIMATION

2010 ◽  
Vol 52 (4) ◽  
pp. 451-468 ◽  
Author(s):  
Hiroaki Ogata ◽  
Masanobu Taniguchi
Keyword(s):  
Empirical Likelihood ◽  
Stationary Processes ◽  
Gaussian Vector ◽  
Minimum Contrast ◽  
Contrast Estimation ◽  
Likelihood Approach ◽  
Non Gaussian

scholarly journals Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Hiroaki Ogata
Keyword(s):  
Empirical Likelihood ◽  
Numerical Study ◽  
Asymptotic Distributions ◽  
Likelihood Method ◽  
Stationary Processes ◽  
Finite Sample ◽  
Empirical Likelihood Method ◽  
Locally Stationary Processes ◽  
Non Gaussian ◽  
Maximum Empirical Likelihood Estimator

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.

scholarly journals Nonparametric Approach for Non-Gaussian Vector Stationary Processes

1996 ◽  
Vol 56 (2) ◽  
pp. 259-283 ◽  
Author(s):  
Masanobu Taniguchi ◽  
Madan L. Puri ◽  
Masao Kondo
Keyword(s):  
Stationary Processes ◽  
Gaussian Vector ◽  
Nonparametric Approach ◽  
Non Gaussian
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