This function computes the estimated mean squared prediction errors from a given time series and prediction coefficients
the data
the array of coefficients.
which lead time to compute the MSPE for
a vector of times from which backward the forecasts are computed
indicating what type of measure of accuracy is to be computed; 1: mspe, 2: msae
percentage of lower observations to be trimmed away
percentage of upper observations to be trimmed away
Returns a P x length(N) matrix with the results.
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).