Abstract
We consider Bayesian analysis of threshold autoregressive moving average model with exogenous inputs (TARMAX). In order to obtain the desired marginal posterior distributions of all parameters including the threshold value of the two-regime TARMAX model, we use two different Markov chain Monte Carlo (MCMC) methods to apply Gibbs sampler with Metropolis-Hastings algorithm. The first one is used to obtain iterative least squares estimates of the parameters. The second one includes two MCMC stages for estimate the desired marginal posterior distributions and the parameters. Simulation experiments and a real data example show support to our approaches.
| Original language | English |
|---|---|
| Pages (from-to) | 1089-1104 |
| Number of pages | 16 |
| Journal | Communications in Statistics - Simulation and Computation |
| Volume | 41 |
| Issue number | 6 |
| Early online date | 10 Feb 2012 |
| DOIs | |
| Publication status | Published - 2012 |
Keywords
- Bayesian analysis
- Gibbs sampler
- Metropolis-Hastings algorithm
- TARMAX model
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Xia, Q., Liu, J., Pan, J., & Liang, R. (2012). Bayesian analysis of two-regime threshold autoregressive moving average model with exogenous inputs. Communications in Statistics - Simulation and Computation, 41(6), 1089-1104. https://doi.org/10.1080/03610926.2010.538792