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FCI对我国通胀的预测效果分析 被引量:6

A Study on the Predictive Performance of FCI for China's Inflation
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摘要 对通胀趋势进行预测是治理通胀的关键。本文首先运用广义脉冲响应函数构建了我国的金融状况指数(FCI),然后从多个角度评估了FCI对我国通胀水平的总体预测效果,随后应用Markov机制转换模型将我国通胀状态的转换概率内生化,并对不同状态下的FCI通胀预测效果进行了比较。结果表明:总体上,金融状况指数是我国通货膨胀率的先行指标,能有效预测未来半年内的通胀趋势;在模拟和预测我国通胀率运行趋势方面,Markov机制转换模型优于线性AR(2)模型;高通胀状态下FCI的通胀预测能力强于低通胀状态;低通胀状态下FCI对短期通胀的预测效果优于中长期。 Inflation prediction is a key to inflation -fighting. At first, this paper puts out a construction of Chinese Fi-nancial Conditions Index (FCI) based on Generalized Impulse - response Functions ( GIRF ). Secondly, it estimates FCI's general predictive effect for Chinese inflation in different ways. Then, the predictive effects of FCI for inflation in different inflation conditions are given after an analysis of Chinese inflation situation switching by using Markov regime - switching model. The results show that FCI has a relatively strong predictive ability for the trend of Chinese inflation rate in the next six months. Markov regime -switching model is better than linear AR(2)model when predicting Chinese in- flation trend. Predictive ability of FCI for Chinese Inflation in high inflation condition is stronger than in low inflation condition. Under the low inflation condition, predictive effect of FCI for inflation in the short term is better than in the medium term and long- term.
出处 《中国软科学》 CSSCI 北大核心 2012年第7期61-70,共10页 China Soft Science
基金 国家社会科学基金项目(批准号:10CJY064) 教育部人文社会科学基金项目(批准号:09YJC790152) 江苏省"青蓝工程"
关键词 金融状况指数 通胀预测 广义脉冲 Markov机制转换模型 financial conditions index inflation prediction generalized impulse Markov regime - switching model
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参考文献10

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