Federal Funds Rate Prediction: A Comparison Between the Robust RBF Neural Network and Economic Models*

Neural network forecasting models have been widely used in the analyses of finan-cial time series during the last decade. This paper attempts to fill this gap in the literature by examining a variety of univariate and multivariate, linear, nonlinear Economics em-pirical modes and neural network. In this paper, we construct an M-estimator based RBF (MRBF) neural network with growing and pruning techniques. Then we compare the forecasting performances of MRBF with six other time-series forecasting models for daily U.S. effective federal funds rate. The results show that the proposed MRBF net-work can produce the lowest root mean square errors in one-day-ahead forecasting for the federal funds rate. It implies that MRBF can be one good method for the predictions of some financial time series data