JISE


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Journal of Information Science and Engineering, Vol. 25 No. 3, pp. 763-778


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

Chien-Cheng Lee1, Chun-Li Tsai2 and Yu-Chun Chiang3
1Department of Communications Engineering 
3Department of Mechanical Engineering and Yuan Ze Fuel Cell Center 
Yuan Ze University 
Chungli, 320 Taiwan 
E-mail: {cclee; ycchiang}@saturn.yzu.edu.tw 
2Department of Economics 
National Cheng Kung University 
Tainan, 701 Taiwan 
E-mail: tchunli@mail.ncku.edu.tw

    Neural network forecasting models have been widely used in the analyses of financial 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 empirical 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 network 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.

Keywords: federal funds rate, time series prediction, M-estimator, RBF, growing and pruning

  Retrieve PDF document (JISE_200903_07.pdf)