BAYSTAR (version 0.2-2)

TAR.coeff: Estimate AR coefficients

Description

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.

Usage

TAR.coeff(reg, ay, p1, p2, sig, lagd, thres, mu0, v0, lagp1, lagp2, constant = 1)

TAR.coeff(reg, ay, p1, p2, sig, lagd, thres, mu0, v0, lagp1, lagp2, constant = 1, thresVar)

Arguments

reg

The regime is assigned. (equal to one or two)

ay

The real data set. (input)

p1

Number of AR coefficients in regime one.

p2

Number of AR coefficients in regime two.

sig

The error terms of TAR model.

lagd

The delay lag parameter.

thres

The threshold parameter.

mu0

Mean vector of conditional prior distribution in mean equation.

v0

Covariance matrix of conditional prior distribution in mean equation.

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 self series are used)

synopsis

TAR.coeff(reg, ay, p1, p2, sig, lagd, thres, mu0, v0, lagp1, lagp2, constant = 1, thresVar)