An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy

Keyi Ju, Bin Su, Dequn Zhou and Yuqiang Zhang

Applied Energy, 2016, vol. 163, issue C, 452-463

Abstract: Different oil price shock incentives under different domestic and international environment will cause different oil price shocks and bring different impacts to China’s macroeconomy. However, there are few empirical studies on early warning prediction of the co-movements between oil price shocks and macroeconomy. This paper presents an incentive-oriented artificial intelligent (AI) early warning system (EWS) with ontology supported case based reasoning (CBR) method, called “relationship between oil price shocks and economy-an early warning system (ROSE2)”, to forecast the co-movements between macroeconomy and oil price shocks in China. Simultaneously, multi-galois lattice (MGL), which is more suitable for matching multiple attributes, is used to improve the recall and precision capability of ROSE2. Finally, several practical queries called Q1–Q4 are presented for verifying the validation and efficiency of the ROSE2 system.

Keywords: Oil price shock incentive; Early warning system; Case based reasoning; Ontology; Oil price shock; Macroeconomy (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.apenergy.2015.11.015

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