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Bayesian inference and forecasts with full range autoregressive time series models


Abstract


En
This paper describes the Bayesian inference and forecasting as applied to the full range autoregressive (FRAR) model. The FRAR model provides an acceptable alternative to the existing methodology. The main advantage associated with the new method is that one is completely avoiding the problem of order determination of the model as in the existing methods.

Keywords: Full range autoregressive model; Posterior distribution; Bayesian analysis; Bayesian predictive distribution

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