computeMSPEcpp: Mean Squared Prediction Errors, for a single h

In forecastSNSTS: Forecasting for Stationary and Non-Stationary Time Series

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,…,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, …, 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}|} ∑_{t \in \mathcal{T}} (X_{t+h} - (X_t, …, 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.

forecastSNSTS documentation built on Sept. 2, 2019, 5:06 p.m.