  Transformed Generalized Autoregressive Moving Average (TGARMA) models were
recently proposed to deal with non-additivity, non-normality and
heteroscedasticity in real time series data. In this paper, a Bayesian approach
is proposed for TGARMA models, thus extending the original model. We conducted
a simulation study to investigate the performance of Bayesian estimation and
Bayesian model selection criteria. In addition, a real dataset was analysed
using the proposed approach.
