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)The regime is assigned. (equal to one or two)
The threshold parameter.
The delay lag parameter.
Number of AR coefficient in regime one.
Number of AR coefficient in regime two.
The vector of AR parameters in regime reg.
The real data set. (input)
The hyper-parameter of Inverse Gamma distribution for priors of variance. (i.e. IG(v/2,lambda/2))
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).
The vector of non-zero autoregressive lags for the upper regime. (regime two)
Use the CONSTANT option to fit a model with/without a constant term (1/0). By default CONSTANT=1.
Exogenous threshold variable. (if missing, the series x is used)