社会经济系统数学模型中推断变结构问题的Bayes预测方法

A PREDICTIVE VIEW OF INFERENCE ABOUT THE STRUCTURAL CHANGE IN MATHEMATICAL MODELS OF SOCIAL ECONOMY

  • 摘要: 本文利用统计诊断的思想,从Bayes预测观点对社会经济系统数学模型中的结构变化问题进行推断,给出了基于条件预测统计量和Kullback-Lebler距离的两种新方法,这些方法均为直接从原始数据出发估计转变点,而不象其它方法那样需在事先选择变结构模型的具体形式,因此计算简便,应用广泛,此外,本文着重研究了我国人均钢材消费与人均国民生产总值之间的关系,获得比较理想的结果.

     

    Abstract: Using some ideas of statistical diagnostics, we analyse structuralchange in mathematical models of social economy from Bayesian predictive point of view. In this paper, we present two new methods which are based on the conditional predictive discordancy diagnostics and Kullback-Leibler divergence. Using these methods, we need no assumption about the structural change before analysing change points (in general methods, the assumption is mecessary, as a change in one or more of the parameters of the model in question). The methods presented are simple and generally acceptable and convenient for computing. Furthermore, we study relationhips between Chinese average steel consumption and average GNP. It is shown the results reflect well the changes of economy relationships by investigating the characteristics of government policies.

     

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