Bayesian Transformed GARMA Models
Applications
2017-01-02 v1
Abstract
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.
Cite
@article{arxiv.1612.09561,
title = {Bayesian Transformed GARMA Models},
author = {Breno S. Andrade and Marinho G. Andrade and Ricardo S. Ehlers},
journal= {arXiv preprint arXiv:1612.09561},
year = {2017}
}