Journal
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume 73, Issue 8, Pages 563-573Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0094965031000136003
Keywords
autoregressive models; Bayesian analysis; Gibbs sampler; predictive distribution
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In this paper we consider a Bayesian analysis for an autoregressive model with random normal coefficients (RCA). For the proposed procedure we use conjugate priors for some parameters and improper vague priors for others. The inference for the parameters is made via Gibbs sampler and the convergence is assessed with multiple chains and Gelman and Rubin criterium. Forecasts are based on the predictive density of future observations. Some remarks are also made regarding order determination and stationarity. Applications to simulated and real series are given.
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