Description Usage Arguments Details Value Author(s) References See Also Examples
This function estimate a Gaussian TAR model using Least Square method given the structural parameters, i.e. the number of regimes, thresholds and autoregressive orders.
1 | LS.norm(Z, X, l, r, K)
|
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 |
The vector containing the autoregressive orders 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.
The function returns the autoregressive coefficients matrix theta and variance weights H. Rows of the matrix theta represent regimes
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
1 2 3 4 5 6 7 8 |
Loading required package: mvtnorm
$theta.est
[,1]
[1,] 1.1860249
[2,] -0.6094069
$h.est
[1] 1.485759 1.996804
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