Restricted Empirical Likelihood Estimation for Time Series Autoregressive Models

In this paper, we first illustrate the restricted empirical likelihood function, as an alternative to the usual empirical likelihood. Then, we use this quasi-empirical likelihood function as a basis for Bayesian analysis of AR( r ) time series models. The efficiency of both the posterior computation...

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Bibliographic Details
Published in:Journal of Statistical Theory and Applications (JSTA) 2021-03, Vol.20 (1), p.11-20
Main Authors: Bayati, Mahdieh, Ghoreishi, S. K., Wu, Jingjing
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive models
Bayesian analysis
EM algorithm
Empirical analysis
Empirical likelihood
Estimating equations
Estimation
Linear functions
Parameters
Research Article
Time series
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Summary:In this paper, we first illustrate the restricted empirical likelihood function, as an alternative to the usual empirical likelihood. Then, we use this quasi-empirical likelihood function as a basis for Bayesian analysis of AR( r ) time series models. The efficiency of both the posterior computation algorithm, when the estimating equations are linear functions of the parameters, and the EM algorithm for estimating hyper-parameters is an appealing property of our proposed approach. Moreover, the competitive finite-sample performance of this proposed method is illustrated via both simulation study and analysis of a real dataset.
ISSN:1538-7887
2214-1766
DOI:10.2991/jsta.d.210121.001