Description Usage Arguments Details Value Author(s) References See Also Examples
This function identify the number of regimes and the corresponding thresholds for a TAR model with Gaussian noise process.
1 | reg.thr.norm(Z, X, n.sim = 500, p.burnin = 0.2, n.thin = 1)
|
Z |
The threshold series |
X |
The series of interest |
n.sim |
Number of iteration for the Gibbs Sampler |
p.burnin |
Percentage of iterations used for Burn-in |
n.thin |
Thinnin factor for the Gibbs Sampler |
The TAR model is given by
X_t=a_0^{(j)} + ∑_{i=1}^{k_j}a_i^{(j)}X_{t-i}+h^{(j)}e_t
when Z_t\in (r_{j-1},r_j] for som j (j=1,\cdots,l). the \{Z_t\} is the threshold process, l is the number of regimes, k_j is the autoregressive order in the regime j. a_i^{(j)} with i=0,1,\cdots,k_j denote the autoregressive coefficients, while h^{(j)} denote the variance weights. \{e_t\} is the Gaussian white noise process N(0,1).
The function returns the identified number of regimes with posterior probabilities and the thresholds with credible intervals.
Hanwen Zhang <hanwenzhang at usantotomas.edu.co>
Nieto, F. H. (2005), Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data. Communications in Statistics. Theory and Methods, 34; 905-930
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