reg.thr.lognorm {TAR}

Identify the number of regimes and the corresponding thresholds for a log-normal TAR model.

Description

This function identify the number of regimes and the corresponding thresholds for a log-normal TAR model.

Usage

reg.thr.lognorm(Z, X, n.sim = 500, p.burnin = 0.2, n.thin = 1)

Arguments

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

Details

The TAR model is given by

log X_t=a_0^{(j)} + \sum_{i=1}^{k_j}a_i^{(j)}log 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).

Value

The function returns the identified number of regimes with posterior probabilities and the thresholds with credible intervals.

Author(s)

Hanwen Zhang <hanwenzhang at usantotomas.edu.co>

References

Nieto, F. H. (2005), Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data. Communications in Statistics. Theory and Methods, 34; 905-930

See Also

LS.norms

Examples

set.seed(12345678)
# Example 1, log-normal TAR model with 2 regimes
Z<-arima.sim(n=400,list(ar=c(0.5)))
l <- 2
r <- 0
K <- c(2,1)
theta <- matrix(c(-1,0.5,0.3,-0.5,-0.7,NA),nrow=l)
H <- c(1, 1.5)
#X <- simu.tar.lognorm(Z,l,r,K,theta,H)
#res <- reg.thr.lognorm(Z,X)
#res$L.est
#res$L.prob
#res$R.est
#res$R.CI