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This function extracts covariance matrix of 1 to h steps ahead forecast errors for
ssarima()
, gum()
, sma()
, es()
and ces()
models.
covar(object, type = c("analytical", "empirical", "simulated"), ...)# S3 method for smooth
covar(object, type = c("analytical", "empirical",
"simulated"), ...)
Model estimated using one of the functions of smooth package.
What method to use in order to produce covariance matrix:
analytical
- based on the state space structure of the model and the
one-step-ahead forecast error. This works for pure additive and pure multiplicative
models. The values for the mixed models might be off.
empirical
- based on the in-sample 1 to h steps ahead forecast errors
(works fine on larger samples);
simulated
- the data is simulated from the estimated model, then the
same model is applied to it and then the empirical 1 to h steps ahead forecast
errors are produced;
Other parameters passed to simulate function (if type="simulated"
is used). These are obs
, nsim
and seed
. By default
obs=1000
, nsim=100
. This approach increases the accuracy of
covariance matrix on small samples and intermittent data;
Scalar in cases of non-smooth functions. (h x h) matrix otherwise.
The function returns either scalar (if it is a non-smooth model) or the matrix of (h x h) size with variances and covariances of 1 to h steps ahead forecast errors. This is currently done based on empirical values. The analytical ones are more complicated.
# NOT RUN {
x <- rnorm(100,0,1)
# A simple example with a 5x5 covariance matrix
ourModel <- ces(x, h=5)
covar(ourModel)
# }
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