Ebook: Posterior analysis of the multiplicative heteroscedasticity model

In this paper, we show how to use Bayesian approach in the multiplicative heteroscedasticity model discussed by [1]. The Gibbs sampler and the Metropolis-Hastings (MH) algorithm are applied to the multiplicative heteroscedasticity model, where some candidate-generating densities are considered in the MH algorithm. We carry out Monte Carlo study to examine the properties of the estimates via Bayesian approach and the traditional counterparts such as the modified two-step estimator (M2SE) and the maximum likelihood estimator (MLE). Our results of Monte Carlo study show that the candidate-generating density chosen in our paper is suitable, and Bayesian approach shows better performance than the traditional counterparts in the criterion of the root mean square error (RMSE) and the interquartile range (IR).