Bayesian Identification of Double Seasonal Autoregressive Time Series Models

Abstract

In this paper, we present a Bayesian methodology to identify the order of DSAR models. Assuming the model errors are normally distributed and using three priors, i.e. natural conjugate, g, and Jeffreys’ priors, on the model parameters, we derive the joint posterior mass function of the model order in a closed-form. Accordingly, the posterior mass function can be investigated and the best order of DSAR model is chosen as a value with the highest posterior probability for the time series being analyzed.

Publication
In Communications in Statistics - Simulation and Computation
Date
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Ayman A. Amin
Assistant Professor of Statistics
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