| computeMSPEcpp {forecastSNSTS} | R Documentation |
Mean Squared Prediction Errors, for a single h
Description
This function computes the estimated mean squared prediction errors from a given time series and prediction coefficients
Arguments
X |
the data |
coef |
the array of coefficients. |
h |
which lead time to compute the MSPE for |
t |
a vector of times from which backward the forecasts are computed |
type |
indicating what type of measure of accuracy is to be computed; 1: mspe, 2: msae |
trimLo |
percentage of lower observations to be trimmed away |
trimUp |
percentage of upper observations to be trimmed away |
Details
The array of prediction coefficients coef is expected to be of
dimension P x P x H x length(N) x length(t) and in the format as
it is returned by the function predCoef. More precisely, for
p=1,\ldots,P and the j.Nth element of N element of
N the coefficient of the
h-step ahead predictor for X_{i+h} which is computed from
the observations X_i, \ldots, X_{i-p+1} has to be available via
coef[p, 1:p, h, j.N, t==i].
Note that t have to be the indices corresponding to the coefficients.
The resulting mean squared prediction error
\frac{1}{|\mathcal{T}|} \sum_{t \in \mathcal{T}} (X_{t+h} - (X_t, \ldots, X_{t-p+1}) \hat v_{N[j.N],T}^{(p,h)}(t))^2
is then stored in the resulting matrix at position (p, j.N).
Value
Returns a P x length(N) matrix with the results.