加入网络搜索行为能提升CPI的预测效果吗
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摘要
传统的CPI预测模型利用的数据主要来源于政府统计局,由于政府统计数据规范、噪音小等特点,使得这样的预测模型在CPI变化不大的时期预测效果较好,但是在CPI变化趋势出现拐点的时期,预测效果往往较差;网络搜索数据作为一种新兴的数据结构类型,被运用到经济社会问题的预测当中,其实时可得的特点,能够提前预测到趋势变化的拐点。因此将网络搜索数据加入到传统的CPI预测模型中,分析增加网络搜索行为能不能提升CPI的预测效果,尤其是在CPI出现拐点的时期。分析结果显示,当CPI趋势出现拐点的时期,在传统预测模型中加入网络搜索数据,的确可以提升模型的预测效果。
The traditional CPI forecast model uses the data mainly from the government bureau of statistics,because the government statistics data have low noise and are regular,which makes the forecasting model predict better in the period of little CPI change,but its effect is poor in the period of CPI turning point. However,network search data as a new type of data structure,which is applied to the forecast of economic and social problems,and its real-time available features,can predict in advance the turning point of the trend.Therefore,this paper adds the network search data to the traditional CPI forecasting model,and analyzes whether increased network search behavior can promote the prediction effect of CPI,especially for the CPI turning point period. The analysis results show that in the period of the turning point of CPI,the network search data can be added to the traditional model,and the model's prediction effect can be promoted.
The traditional CPI forecast model uses the data mainly from the government bureau of statistics,because the government statistics data have low noise and are regular,which makes the forecasting model predict better in the period of little CPI change,but its effect is poor in the period of CPI turning point. However,network search data as a new type of data structure,which is applied to the forecast of economic and social problems,and its real-time available features,can predict in advance the turning point of the trend.Therefore,this paper adds the network search data to the traditional CPI forecasting model,and analyzes whether increased network search behavior can promote the prediction effect of CPI,especially for the CPI turning point period. The analysis results show that in the period of the turning point of CPI,the network search data can be added to the traditional model,and the model's prediction effect can be promoted.
引文
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[13]李庆华.基于VAR模型的中国消费价格指数分析[J].华中师范大学学报(人文社会科学版),2006(4):56-61.
[2]龚玉婷,陈强,郑旭.基于混频模型的CPI短期预测研究[J].统计研究,2014(12):25-31.
[3]吴强,付永利.中国近年通胀原因与2013年CPI预测分析[J].经济问题,2013(3):51-55.
[4]董梅.基于VAR模型的居民消费价格指数预测[J].统计与决策,2011(1):29-31.
[5]门小琳.组合预测方法在我国CPI预测中的应用[D].南京财经大学,2012.
[6]张崇,吕本富,彭赓,刘颖.网络搜索与CPI的相关性研究[J].管理科学学报,2012(7):50-59.
[7]刘颖,吕本富,彭赓,张崇.网络搜索对股票市场的预测能力:理论分析与实证检验[J].经济管理,2011(1):172-180.
[8]袁庆玉,彭赓,刘颖,吕本富.基于网络关键词搜索数据的汽车销量预测研究[J].管理学家学术版,2011(01):12-24.
[9]孙毅,戴维,董纪昌,吕本富.基于主成分分析的网络搜索数据合成方法研究[J].数学的实践与认识,2014(21):121-128.
[10]高玲,李时椿.关于我国近年货币供给和CPI的实证分析[J].产业与信息论坛,2008(11):151-153.
[11]王众,魏慧丰,郭希明.居民消费价格影响因素的协整分析[J].统计与决策,2006(9):61-62.
[12]王军平.住房价格上涨对CPI的传导效应——兼论我国CPI编制体系的缺陷[J].经济学家,2006(6):78-82.
[13]李庆华.基于VAR模型的中国消费价格指数分析[J].华中师范大学学报(人文社会科学版),2006(4):56-61.