model
{
	Tau.noninformative <- 1.0E-4
	for (i in 1:N.id) {
		Y[1, i] ~ dnorm(0, Tau.noninformative)
		log.y[1, i] ~ dnorm(0, Tau.noninformative)
		for (t in 2:N.year) {
			Y[t, i] ~ dnorm(y[t, i], tau[1])
			log(y[t, i]) <- log.y[t, i]
			log.y[t, i] ~ dnorm(m[t, i], tau[2])
			m[t, i] <- log.y[t - 1, i] + beta + r[i]
		}
	}
	beta ~ dnorm(0.0, Tau.noninformative)
	for (i in 1:N.id) {
		r[i] ~ dnorm(0.0, tau[3])
	}
	for (k in 1:N.tau) {
		tau[k] <- 1 / (sd[k] * sd[k])
		sd[k] ~ dunif(0, 1.0E+4)
	}
}