Conclusion

The focus of this research was in finding an effective model for predicting US federal funds target rate using neural networks . Macroeconomic data represented with fifteen input variables were used as the input, while the output was the federal funds target rate. Three NN algorithms were tested including multilayer perceptron, radial basis, and generalized regression resulting with three different NN models. Individual model results show that the radial basis neural network outperforms other models in the sense of the generalization error. The average error obtained by a 10-fold cross validation procedure showed that the radial basis function network has the smallest average mean RMSE of all three models. The selection of input variables in the best model shows that the most important predictors of the federal funds target rate are discount rate, 3-month treasury rate, 10-year treasury rate, oil price, and production price index .
As guidelines for futher research, we suggest to test time-seria models with lags and multiple time-horizons (1-month, 3-months, and 6-months ahead) using radial basis and recurrent neural networks, to evaluate generalization ability using more samples, and to test the methodology on more datasets from other countries in order to compare the importance of predictor variables accross coutries.