D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy

Mingzhu Zhang, Changzheng He, Xin Gu, Panos Liatsis and Bing Zhu

Economic Modelling, 2013, vol. 30, issue C, 514-520

Abstract: This work proposes a new forecasting model to analyse the economic development of Sichuan province of China. The model, which introduces the concept of diversity, is based on an improvement of the -GMDH algorithm. The new method, called D-GMDH, is compared with two ensemble approaches which are introduced by Dutta (2009), and D-GMDH is better than the two approaches in forecasting accuracy. D-GMDH is also applied to forecast the industrial added value of the Sichuan province. The obtained results are compared with those of the traditional GMDH model, GMDH combination model and the widely used ARMA model. The results show that D-GMDH has good prediction accuracy and is an effective means for economic forecasting when data is contaminated by noise.

Keywords: Economic forecasting; Noise; GMDH; Diversity (search for similar items in EconPapers)
JEL-codes: C45 E17 L16 (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:30:y:2013:i:c:p:514-520

DOI: 10.1016/j.econmod.2012.09.021

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