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Unified knowledge based economy hybrid forecasting

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journal contribution
posted on 14.10.2016, 13:32 by Ahmad Al-Shami, Ahmad Lofti, Simeon ColemanSimeon Coleman, Petr Dostal
Many synthetic composite indicators have been developed with the aim to measure micro- and macro-knowledge competitiveness, however, without any unified, easy to visualise and assessable forecasting capability, their benefits to decision makers remain limited. In this article, a new framework for forecasting knowledge based economy (KBE) competitiveness is proposed. Existing KBE indicators from internationally recognised organisations are used to forecast and unify the KBE performance indices. Three different forecasting methods including time-series cross sectional (TSCS) (also known as panel data), linear multiple regression (LMREG), and artificial neural network (ANN) are employed. The ANN forecasting model outperformed the TSCS and LMREG. The proposed KBE hybrid forecasting model utilises a 2-stage ANN model which is fed with a panel data set structure. The first stage of the model consists of a feed-forward neural network that feeds to a Kohonen's self-organising map (SOM) in the second stage of the model. A feed-forward neural network is used to learn and predict the scores of nations using past observed data. Then, a SOM is used to aggregate the forecasted scores and to place nations in homogeneous clusters. The proposed framework can be applied in the context of forecasting and producing a unified meaningful map that places any KBE in its homogeneous league, even when considering a limited data set.

History

School

  • Business and Economics

Department

  • Economics

Published in

Technological Forecasting and Social Change

Volume

91

Pages

107 - 123

Citation

AL-SHAMI, A. ...et al., 2015. Unified knowledge based economy hybrid forecasting. Technological Forecasting and Social Change, 91, pp.107-123.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

31/01/2014

Publication date

2014-02-22

Notes

This paper was accepted for publication in the journal Technological Forecasting and Social Change and the definitive published version is available at http://dx.doi.org/10.1016/j.techfore.2014.01.014

ISSN

0040-1625

Language

en