Dynamic Bayesian network model for inflation risk warning

Authors:
Wang Shuangcheng;Cheng Xinzhang;Leng Cuiping
Affiliations:
School of mathematics and information, Shanghai Lixin University of Commerce, Shanghai, China and Opening Economy & Trade Research Center, Shanghai Lixin University of Commerce, Shanghai, China;Opening Economy & Trade Research Center, Shanghai Lixin University of Commerce, Shanghai, China;School of mathematics and information, Shanghai Lixin University of Commerce, Shanghai, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

At present, the methods of risk warning emphasize static function dependency or dynamic propagation of time series, which results in a unconsistent combination of the static and dynamic information. Accordingly, this paper puts forward a dynamic hierarchical naive Bayesian network classifier for warning inflation risk. And an example is presented to explain the process of inflation risk warning and method of contribution analysis to risk rank forecast. This model features universality and can be widely used in other risk warning domains.