Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Forecasting daily time series using periodic unobserved components time series models
Computational Statistics & Data Analysis
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
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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.