We assume a normal prior for the AR coefficients and draw AR coefficients from a multivariate normal posterior distribution. Parsimonious subset AR could be assigned in each regime in the BAYSTAR function rather than a full AR model.
TAR.coeff(reg, ay, p1, p2, sig, lagd,
thres, mu0, v0, lagp1, lagp2, constant = 1, thresVar)The regime is assigned. (equal to one or two)
The real data set. (input)
Number of AR coefficients in regime one.
Number of AR coefficients in regime two.
The error terms of TAR model.
The delay lag parameter.
The threshold parameter.
Mean vector of conditional prior distribution in mean equation.
Covariance matrix of conditional prior distribution in mean equation.
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 self series are used)