Multivariate Innovations
Function mvInnovations computes the multivariate versions of one
step-ahead prediction errors and their variances using the output of KFS.
mvInnovations(x)
x |
Object of class |
v |
Multivariate prediction errors v[t,i] = y[t] - Z[t]a[t] |
F |
Prediction error variances Var(v[t]). |
Finf |
Diffuse part of F[t]. |
# Compute the filtered estimates based on the KFS output
filtered <- function(x) {
innov <- mvInnovations(x)
att <- window(x$a, end = end(x$a) - 1)
tvz <- attr(x$model,"tv")[1]
for (i in 1:nrow(att)) {
att[i,] <- att[i,] +
x$P[,,i] %*%
t(solve(innov$F[,,i], x$model$Z[, , tvz * (i - 1) + 1, drop = FALSE])) %*%
innov$v[i, ]
}
att
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