TAR.sigma | R Documentation |
We employ a conjugate prior, Inverse-Gamma distribution, for sigma squared in regime j, j=1,2. To draw the variance of error distribution from an Inverse-Gamma posterior distribution.
TAR.sigma(reg, ay, thres, lagd, p1, p2, ph, v, lambda, lagp1, lagp2, constant = 1, thresVar)
A list containing:
reg |
The regime is assigned. (equal to one or two) |
thres |
The threshold parameter. |
lagd |
The delay lag parameter. |
p1 |
Number of AR coefficient in regime one. |
p2 |
Number of AR coefficient in regime two. |
ph |
The vector of AR parameters in regime |
ay |
The real data set. (input) |
v, lambda |
The hyper-parameter of Inverse Gamma distribution for priors of variance. (i.e. IG(v/2,lambda/2)) |
lagp1 |
The vector of non-zero autoregressive lags for the lower regime. (regime one); e.g. An AR model with p1=3, it could be non-zero lags 1,3, and 5 would set lagp1<-c(1,3,5). |
lagp2 |
The vector of non-zero autoregressive lags for the upper regime. (regime two) |
constant |
Use the CONSTANT option to fit a model with/without a constant term (1/0). By default CONSTANT=1. |
thresVar |
Exogenous threshold variable. (if missing, the series x is used) |
Cathy W.S. Chen, Edward Lin
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