Andreeski, Cvetko and Dimirovski, Georgi and Petreski, Goran (2006) A case study of post-stabilization inflation dynamics via neural-nets approach. In: IEEE International Conference on Computational Cybernetics ICCC 2003, At Siofok, Hungary, Volume: Proceedings of the IEEE International Conference on Computational Cybernetics ICCC 2003.
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Abstract
The recent emerging trend in financial systems engineering relies on exploiting soft-computing technologies, and on employing neural-nets techniques, in particular. Simultaneously, recently experienced observation studies on economy 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 variablesand models provided by economy 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. Both capturing the empirically established phenomenon in inflationary processes and its use in forecasting based decision and control policies became feasible. The approach is illustrated by the case-study on inflation dynamics in the pre- and post-stabilization period of the nineties in the Republic of Macedonia.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Scientific Fields (Frascati) > Social Sciences > Economics and Business |
| Divisions: | Faculty of Tourism and Hospitality |
| Depositing User: | Mr Bojan Sekulovski |
| Date Deposited: | 25 Oct 2017 10:57 |
| Last Modified: | 06 Nov 2017 07:34 |
| URI: | https://eprints.uklo.edu.mk/id/eprint/666 |
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