data(nb_mat) # Get neighbourhood matrices
# Simulate a STARMA model
eps <- matrix(rnorm(94*200), 200, 94)
sim <- eps
for (t in 3:200) {
sim[t,] <- (.4*diag(94) + .25*blist[[2]]) %*% sim[t-1,] +
(.25*diag(94) ) %*% sim[t-2,] +
( - .3*blist[[2]]) %*% eps[t-1,] +
eps[t, ]
}
sim <- sim[101:200,]
sim <- stcenter(sim) # Center and scale the dataset
# Select parameters to estimate
ar <- matrix(0, 2, 2)
ar[ ,1] <- 1 # phi10 and phi20
ar[1,2] <- 1 # phi11
ma <- matrix(c(0,1), 1, 2) # theta11
# Run the Kalman filter algorithm
model <- starma(sim, blist, ar, ma)
# Get summary
summary(model)
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