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Copyright (c) 2017 Pakistan Journal of Statistics and Operation Research

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Title

Bayesian Inference for Double Seasonal Moving Average Models: A Gibbs Sampling Approach

Keywords

Multiplicative seasonal moving average, Double seasonality, Bayesian analysis, Gibbs sampler

Description

In this paper we use the Gibbs sampling algorithm to develop a Bayesian inference for multiplicative double seasonal moving average (DSMA) models. Assuming the model errors are normally distributed and using natural conjugate priors, we show that the conditional posterior distribution of the model parameters and variance are multivariate normal and inverse gamma respectively, and then we apply the Gibbs sampling to approximate empirically the marginal posterior distributions. The proposed Bayesian methodology is illustrated using simulation study.

Date

2017-08-31

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 13 No. 3, 2017



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810