comb_InvW() R function from [GeomComb]

Inverse Rank Forecast Combination

Computes forecast combination weights according to the inverse rank approach by Aiolfi and Timmermann (2006) and produces forecasts for the test set, if provided.


comb_InvW(x)

Arguments

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

Returns

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.

Details

In the inverse rank approach by Aiolfi and Timmermann (2006), the combination weights are inversely proportional to the forecast model's rank, $Rank_i$ :

w_i^{InvW} = \frac{Rank_i^{-1}}{\Sigma_{j=1}^N Rank_j^{-1}}w_i = Rank_i^{-1} / \Sigma_{j=1}^N Rank_j^{-1}

The combined forecast is then obtained by:

\hat{y}_t = {\mathbf{f}_{t}}'\mathbf{w}^{InvW}\hat{y}_t = (f_t)'w

This is a robust variant of the Bates/Granger (1969) approach and also ignores correlations across forecast errors.

Examples


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_InvW(data)

Author(s)

Christoph E. Weiss and Gernot R. Roetzer

References

Aiolfi, M., amd Timmermann, A. (2006). Persistence in Forecasting Performance and Conditional Combination Strategies. Journal of Econometrics, 135(1) , 31--53.

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

comb_InvW function