A Bayesian analysis of moving average processes with time-varying parameters

A new Bayesian method is proposed for estimation and forecasting with Gaussian moving average (MA) processes with time-varying parameters. The focus is placed on MA models of order one, but a general result is given for an MA process of an arbitrary known order. A multiplicative model for the evolution of the squares of the parameters is introduced following Bayesian conjugacy through beta and truncated gamma distributions and a discount factor. Two new distributions are proposed providing the prior and posterior distributions of the parameters of the model and the one-step forecast distribution of the process. Several well-known distributional results are extended by replacing the gamma distribution with the truncated gamma distribution. The proposed methodology is illustrated with two examples consisting of simulated data and of aluminium spot prices of the London metal exchange. (c) 2007 Elsevier B.V. All rights reserved.