Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/simu.tar.lognorm.R
This function simulates a serie from a log-normal TAR model with Gaussian distributed error given the parameters of the model from a given threshold process \{Z_t\}
1 | simu.tar.lognorm(Z, l, r, K, theta, H)
|
Z |
The threshold series |
l |
The number of regimes. |
r |
The vector of thresholds for the series \{Z_t\}. |
K |
The vector containing the autoregressive orders of the l regimes. |
theta |
The matrix of autoregressive coefficients of dimension l\times\max{K}. j-th row contains the autoregressive coefficients of regime j. |
H |
The vector containing the variance weights of the l regimes. |
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 time series \{X_t\}.
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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