DocumentCode :
1942474
Title :
Modeling Short Term Interest Rates: A Comparison of Methodologies
Author :
Malliaris, A.G. ; Malliaris, Mary
Author_Institution :
Loyola Univ. Chicago, Chicago
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
732
Lastpage :
736
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 short term interest rates. In this study we use four competing methodologies that model the behavior of short term 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.
Keywords :
economic forecasting; economic indicators; neural nets; time series; Federal Open Market Committee; Taylor rule methodology; econometric; future short term interest rates prediction; neural network; short term interest rates modeling; time series; Econometrics; Economic forecasting; Economic indicators; Information systems; Input variables; Neural networks; Proposals; Stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371048
Filename :
4371048
Link To Document :
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