NDLI: How Good ANN Identification of Post-Stabilization Inflation Dynamics Can Be?

Content Provider	IEEE Xplore Digital Library
Author	Dimirovski, G.M. Andreeski, C.J.
Copyright Year	2006
Description	Author affiliation: Dogus Univ., Istanbul (Dimirovski, G.M.)
Abstract	The recent emerging trend in financial systems engineering relies on exploiting soft-computing technologies, and on employing neural-nets techniques, in particular. Simultaneously, recent empirical studies on economic stabilization programs implemented worldwide have clearly demonstrated that, after the successful disinflation, the inflationary process can no longer be captured and explained using the traditional variables and models provided by economic theory. This paper proposes a combined stochastic and artificial neural-nets approach in expert support systems to the identification of inflation dynamics by means of Box-Jenkins ARIMA and Elman-ANN models. The approach is illustrated by means of the case-study data set on inflation dynamics in the pre-and post-stabilization period in the Republic of Macedonia.
Starting Page	2098
Ending Page	2105
File Size	285784
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246980
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economic forecasting Artificial neural networks Time series analysis Nonlinear dynamical systems Pattern analysis Pattern recognition Systems engineering and theory Stochastic systems Helium Predictive models
Content Type	Text
Resource Type	Article

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