A Multi-Stage Procedure of Bayesian Estimation for First Order Moving Average Model

Abstract

In this paper we present a multi-stage procedure for improving the Bayes estimates of first order moving average model (MA(1)) using the estimated residuals.  Simulation results based on different model structures with varying numbers of observations are used to investigate the performance of the proposed procedure. The results show a remarkable improvement of the t-approximation using two and three stage procedure in terms of the posterior mean and variance for different model structures. It is also noted that these estimates converge as the series lengths increase.