ForecastComb (version 1.3.1)

comb_InvW: Inverse Rank Forecast Combination

Description

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

Usage

comb_InvW(x)

Arguments

x

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

Value

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, \(Rank_i\):

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

The combined forecast is then obtained by:

$$\hat{y}_t = {\mathbf{f}_{t}}'\mathbf{w}^{InvW}$$

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

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.

See Also

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

Examples

# NOT RUN {
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)

# }