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 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 finitesample performance of this proposed method is illustrated via both simulation study and analysis of a real dataset.