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

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  • Ju, Keyi
  • Su, Bin
  • Zhou, Dequn
  • Zhang, Yuqiang

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.

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  • Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
    • Handle: RePEc:eee:appene:v:163:y:2016:i:c:p:452-463
    DOI: 10.1016/j.apenergy.2015.11.015
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