model {for (t in 2:500) {
y[t] ~ dnorm(0,P[t])
h[t] <- gam+alph*pow(y[t-1],2)+beta*h[t-1]
P[t] <- 1/h[t]}
h[1] <- h.1
h.1 ~ dgamma(1,1)
gam ~ dgamma(1,1)
alph ~ dnorm(0,1) I(0,)
beta ~ dnorm(0,1) I(0,)
sumcoef <- alph+beta
Stat <- step(1-sumcoef)}
