Bayesian prediction in threshold autoregressive models with exponential white noise
In this paper, we develop a Bayesian analysis of a threshold antoregressive model with exponential noise. An approximate Bayes methodology, which is introduced here; and the Gibbs sampler are used to compute marginal posterior densities for the parameters of the model; including the threshold parame...
| Autor principal: | |
|---|---|
| Outros Autores: | |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2011
|
| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10773/4433 |
| País: | Portugal |
| Oai: | oai:ria.ua.pt:10773/4433 |
| Resumo: | In this paper, we develop a Bayesian analysis of a threshold antoregressive model with exponential noise. An approximate Bayes methodology, which is introduced here; and the Gibbs sampler are used to compute marginal posterior densities for the parameters of the model; including the threshold parameter, and to compute one-step-ahead predictive density functions. The proposed methodology is illustrated with a simulation study and a real example. |
|---|