Federal funds rate prediction: A comparison between the robust RBF neural network and economic models

Chien Cheng Lee, Chun-Li Tsai, Yu Chun Chiang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)763-778
Number of pages16
JournalJournal of Information Science and Engineering
Volume25
Issue number3
Publication statusPublished - 2009 May

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Software
  • Library and Information Sciences
  • Human-Computer Interaction
  • Computational Theory and Mathematics

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