comb_NG: Newbold/Granger (1974) Forecast Combination
In ForecastComb: Forecast Combination Methods

Description Usage Arguments Details Value Author(s) References See Also Examples

Computes forecast combination weights according to the approach by Newbold and Granger (1974) and produces forecasts for the test set, if provided.

1	comb_NG(x)

`x`	An object of class `foreccomb`. Contains training set (actual values + matrix of model forecasts) and optionally a test set.

Building on early research by Bates and Granger (1969), the methodology of Newbold and Granger (1974) also extracts the combination weights from the estimated mean squared prediction error matrix.

Suppose y_t is the variable of interest, there are N not perfectly collinear predictors, f_t = (f_{1t}, …, f_{Nt})', Σ is the (positive definite) mean squared prediction error matrix of f_t and e is an N * 1 vector of (1, …, 1)'.

Their approach is a constrained minimization of the mean squared prediction error using the normalization condition e'w = 1. This yields the following combination weights:

w = (Σ^{-1} * e) / (e' * Σ^{-1} * e)

The combined forecast is then obtained by:

\hat{y}_t = (f_t)'w

While the method dates back to Newbold and Granger (1974), the variant of the method used here does not impose the prior restriction that Σ is diagonal. This approach, called VC in Hsiao and Wan (2014), is a generalization of the original method.

Returns an object of class foreccomb_res with the following components:

`Method`	Returns the used forecast combination method.
`Models`	Returns the individual input models that were used for the forecast combinations.
`Weights`	Returns the combination weights obtained by applying the combination method to the training set.
`Fitted`	Returns the fitted values of the combination method for the training set.
`Accuracy_Train`	Returns range of summary measures of the forecast accuracy for the training set.
`Forecasts_Test`	Returns forecasts produced by the combination method for the test set. Only returned if input included a forecast matrix for the test set.
`Accuracy_Test`	Returns range of summary measures of the forecast accuracy for the test set. Only returned if input included a forecast matrix and a vector of actual values for the test set.
`Input_Data`	Returns the data forwarded to the method.

Christoph E. Weiss and Gernot R. Roetzer

Bates, J. M., and Granger, C. W. (1969). The Combination of Forecasts. Journal of the Operational Research Society, 20(4), 451–468.

Hsiao, C., and Wan, S. K. (2014). Is There An Optimal Forecast Combination? Journal of Econometrics, 178(2), 294–309.

Newbold, P., and Granger, C. W. J. (1974). Experience with Forecasting Univariate Time Series and the Combination of Forecasts. Journal of the Royal Statistical Society, Series A, 137(2), 131–165.

comb_BG, comb_EIG1, foreccomb, plot.foreccomb_res, summary.foreccomb_res, accuracy

obs <- rnorm(100)
preds <- matrix(rnorm(1000, 1), 100, 10)
train_o<-obs[1:80]
train_p<-preds[1:80,]
test_o<-obs[81:100]
test_p<-preds[81:100,]

data<-foreccomb(train_o, train_p, test_o, test_p)
comb_NG(data)