Description Usage Arguments Details Value Author(s) References See Also Examples
This function identify the autoregressive orders for a TAR model with Gaussian noise process given the number of regimes and thresholds.
1 2 | ARorder.norm(Z, X, l, r, k_Max = 3, k_Min = 0, n.sim = 500,
p.burnin = 0.3, n.thin = 1)
|
Z |
The threshold series |
X |
The series of interest |
l |
The number of regimes. |
r |
The vector of thresholds for the series {Z_t}. |
k_Max |
The minimum value for each autoregressive order. The default is 3. |
k_Min |
The maximum value for each autoregressive order. The default is 0. |
n.sim |
Number of iteration for the Gibbs Sampler |
p.burnin |
Percentage of iterations used for |
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 identified autoregressive orders with posterior probabilities
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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