TAR.sigma {BAYSTAR}

To draw the variance of error distribution.

Description

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.

Usage

TAR.sigma(reg, ay, thres, lagd, p1, p2, ph, v, 
          lambda, lagp1, lagp2, constant = 1, thresVar)

Arguments

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 reg.

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)

Author(s)

Cathy W.S. Chen, Edward Lin