Bayesian analysis of autoregressive fractionally integrated moving‐average processes
Copyright policy )Bayesian analysis of autoregressive fractionally integrated moving‐average processes
For the autoregressive fractionally integrated moving‐average (ARFIMA) processes which characterize both long‐memory and short‐memory behavior in time series, we formulate Bayesian inference using Markov chain Monte Carlo methods. We derive a form for the joint posterior distribution of the parameters that is computationally feasible for repetitive evaluation within a modified Gibbs sampling algorithm that we employ. We illustrate our approach through two examples.
- University of Connecticut United States
- University of Manitoba Canada
long-memory models, exact likelihood, Time series, auto-correlation, regression, etc. in statistics (GARCH), Gibbs sampling, Bayesian inference, posterior distribution, Markov chain Monte Carlo methods, time series
long-memory models, exact likelihood, Time series, auto-correlation, regression, etc. in statistics (GARCH), Gibbs sampling, Bayesian inference, posterior distribution, Markov chain Monte Carlo methods, time series
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