Trimmed Bias-Corrected Eigenvector Forecast Combination
Computes forecast combination weights according to the trimmed bias-corrected eigenvector approach by Hsiao and Wan (2014) and produces forecasts for the test set, if provided.
comb_EIG4(x, ntop_pred = NULL, criterion = "RMSE")
x: An object of class foreccomb. Contains training set (actual values + matrix of model forecasts) and optionally a test set.ntop_pred: Specifies the number of retained predictors. If NULL (default), the inbuilt optimization algorithm selects this number.criterion: If ntop_pred is not specified, a selection criterion is required for the optimization algorithm: one of "MAE", "MAPE", or "RMSE". If ntop_pred is selected by the user, criterion should be set to NULL (default).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.
Intercept: Returns the intercept (bias correction).
Weights: Returns the combination weights obtained by applying the combination method to the training set.
Top_Predictors: Number of retained predictors.
Ranking: Ranking of the predictors that determines which models are removed in the trimming step.
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.
The underlying methodology of the trimmed bias-corrected eigenvector approach by Hsiao and Wan (2014) is the same as their bias-corrected eigenvector approach. The only difference is that the bias-corrected trimmed eigenvector approach pre-selects the models that serve as input for the forecast combination, only a subset of the available forecast models is retained, while the models with the worst performance are discarded.
The number of retained forecast models is controlled via ntop_pred. The user can choose whether to select this number, or leave the selection to the inbuilt optimization algorithm (in that case ntop_pred = NULL). If the optimization algorithm should select the best number of retained models, the user must select the optimization criterion: MAE, MAPE, or RMSE. After this trimming step, the weights, the intercept and the combined forecast are computed in the same way as in the bias-corrected eigenvector approach.
The bias-corrected trimmed eigenvector approach combines the strengths of the
bias-corrected eigenvector approach and the trimmed eigenvector approach.
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,] ## Number of retained models selected by the user: data<-foreccomb(train_o, train_p, test_o, test_p) comb_EIG4(data, ntop_pred = 2, criterion = NULL) ## Number of retained models selected by algorithm: data<-foreccomb(train_o, train_p, test_o, test_p) comb_EIG4(data, ntop_pred = NULL, criterion = "RMSE")
Christoph E. Weiss and Gernot R. Roetzer
Hsiao, C., and Wan, S. K. (2014). Is There An Optimal Forecast Combination? Journal of Econometrics, 178(2) , 294--309.
comb_EIG2
comb_EIG3
foreccomb, plot.foreccomb_res, summary.foreccomb_res, accuracy