Article ID: | iaor1997788 |
Country: | United Kingdom |
Volume: | 15 |
Issue: | 2 |
Start Page Number: | 63 |
End Page Number: | 82 |
Publication Date: | Mar 1996 |
Journal: | International Journal of Forecasting |
Authors: | Pai Jeffrey S., Ravishanker Nalini |
Keywords: | markov processes |
This article describes inference for autoregressive fractionally integrated moving average (ARFIMA) models using Markov chain Monte Carlo methods. The posterior distribution of the model parameters, corresponding to the exact likelihood function is obtained through the partial linear regression coefficients of the ARFIMA process. A Metropolis-Rao-Blackwellizallization approach is used for implementing sampling-based Bayesian inference. Bayesian model selection is discussed and implemented.