Modeling Federal Funds Rates: A Comparison of Four Methodologies

Abstract

The celebrated Taylor Rule methodology has established that the decisions made by the Federal Open Market Committee concerning possible changes in short term interest rates reflected in Fed Funds are influenced by deviations from a desired level of inflation and from potential output. The Taylor Rule determines the future interest rate and is one among several methodologies than can be used to predict future Federal Funds. In this study we use four competing methodologies that model the behavior of Fed Fund interest rates. These methodologies are: time series, Taylor, econometric and neural network. Using monthly data from 1958 to the end of 2005 we distinguish between sample and out-of-sample sets to train, evaluate, and compare the models' effectiveness. Our results indicate that the econometric modeling performs better than the other approaches when the data are divided into two sets of pre-Greenspan and Greenspan periods. However, when the data sample is divided into three groups of low, medium and high Federal Funds, the neural network approach does best.

Keywords: Federal Funds, Modeling Interest Rates, Taylor Rule, Neural Networks

JEL Classification: E47, E52, G10