comb_InvW: Inverse Rank 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 inverse rank approach by Aiolfi and Timmermann (2006) and produces forecasts for the test set, if provided.

1	comb_InvW(x)

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

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 = Rank_i^{-1} / Σ_{j=1}^N Rank_j^{-1}

The combined forecast is then obtained by:

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

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

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

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.

foreccomb, plot.foreccomb_res, summary.foreccomb_res, comb_BG, 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_InvW(data)