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A Mining Driven Decision Support System for Joining the European Monetary Union

Authors:
Ray Hashemi
Omid Ardakani
Azita Bahrami
Jeffrey Young
Rosina Campbell

Keywords: Mining Features; Mining-based Decision Support System; The European Monetary Union; Bayesian Theorem

Abstract:
The European Monetary Union (EMU) is a result of an economic integration of European Union member states into a unified economic system. The literature is divided on whether the EMU members benefit from this monetary unification. Considering costs and benefits, a fiscal authority may ask whether it is a good decision to join the EMU. We introduce and develop a decision support system to answer the proposed question using a historical dataset of twelve Macroeconomic Outcomes (MOs) obtained for 31 European countries and for 18 years (1999-2016). The system meets the three-prong goal of: (1) identifying highly relevant MOs for a given year, yi, using the data from years y1 to yi; (2) deriving decision of “join/not-join” the EMU along with its certainty factor using the relevant MOs for yi; and (3) examining the accuracy of the derived decision using the data from yi+1 to y18. The performance analysis of the system reveals that (a) the number of relevant MOs has declined nonlinearly over time, (b) the relevant MOs and decisions are significantly changed before and after the European debt crisis, and (c) the derived decisions by the system has 79% accuracy.

Pages: 39 to 45

Copyright: Copyright (c) IARIA, 2018

Publication date: July 22, 2018

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-654-5

Location: Barcelona, Spain

Dates: from July 22, 2018 to July 26, 2018