TAR.thres: To draw a threshold value.

Description

The prior for the threshold parameter $thres$, follows a uniform prior on a range (l,u), where l and u can be set as relevant percentiles of the observed threshold variable. This prior could be considered to correspond to an empirical Bayes approach, rather than a fully Bayesian one. The posterior distribution of $thres$ is not of a standard distributional form, thus requiring us to use the Metropolis-Hastings (MH) method to achieve the desired sample for $thres$.

Usage

TAR.thres(ay, p1, p2, ph.1, ph.2, sig.1, sig.2, lagd, thres, step.r = 0.02, bound, lagp1, lagp2, constant = 1)

TAR.thres(ay, p1, p2, ph.1, ph.2, sig.1, sig.2, lagd, thres, step.r = 0.02, bound, lagp1, lagp2, constant = 1, thresVar)

Arguments

The real data set. (input)

Number of AR coefficients in regime one.

Number of AR coefficients in regime two.

ph.1

The vector of AR parameters in regime one.

ph.2

The vector of AR parameters in regime two.

sig.1

The error terms of AR model in the regime one.

sig.2

The error terms of AR model in the regime two.

lagd

The delay lag parameter.

thres

The threshold parameter.

step.r

Step size of threshold variable for the MH algorithm are controlled the proposal variance.

bound

The bound of threshold parameter.

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)

synopsis

TAR.thres(ay, p1, p2, ph.1, ph.2, sig.1, sig.2, lagd, thres, step.r = 0.02, bound, lagp1, lagp2, constant = 1, thresVar)