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