我国生猪价格的影响因素是时变的吗?基于动态模型平均的分析与预测
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作者单位:

1.湖北大学 乡村振兴研究院;2.华中农业大学 经济管理学院

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F 306

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目),中国博士后科学基金


Do The Influencing Factors of Hog Price Change Over Time: The Analysis and Forecasting based on Dynamic Model Averaging
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1.Rural Revitalization Institute, Hubei University;2.College of Economics and Management, Huazhong Agricultural University

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    摘要:

    近年来,我国生猪价格的超常波动给人民生活与相关产业发展带来不利影响。为此,有效识别生猪价格影响因素并对生猪价格进行准确预测,具有重要现实意义。本文从生猪价格影响因素的时变特征入手,提出一套基于动态模型平均理论的生猪价格影响因素与预测分析框架。具体的,本文考虑生猪供给、生猪需求、我国经济环境和国际市场等四方面的13个生猪价格影响因素,研究并识别生猪价格影响因素的时变特征,进而构建生猪价格预测模型,并通过预测误差指标和DM检验比较其与基准模型的预测能力。研究发现:我国生猪价格影响因素存在显著的时变特征,且因素间差异明显;自2009年以来,生猪价格的决定机制更为复杂,影响因素更为多元;基于动态模型平均的生猪价格预测模型的预测表现明显优于基准模型。

    Abstract:

    In recent years, the extraordinary fluctuations of hog price in China exert adverse effects on people’s livelihood and the development of related industries. Thus, exploring the influencing factors of hog price and obtaining the accurate forecasts of hog price are of great practical significance. In view of the time-varying characteristics of hog price's influencing factors, this paper puts forward an influencing factors analysis and hog price forecasting framework based on dynamic model averaging theory. Specifically, In view of thirteen influencing factors derived from the supply of hog, demand of hog, economics environment of China, and international markets, this paper investigates the time-varying characteristics of influencing factors, and then construct the hog price forecasting model, by introducing the dynamic model averaging. Furthermore, we compare the ability of the proposed model and benchmarks used to forecast the hog price by accuracy measures and DM test. The empirical results indicate that the influencing factors of hog price exhibit significant time-varying characteristics. Since 2009, the decision mechanism and influencing factors of hog price have become more complicated and diverse, respectively. Furthermore, the hog price forecasting model based on dynamic model averaging achieves more accurate forecasts than benchmarks.

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  • 收稿日期:2019-12-05
  • 最后修改日期:2020-03-23
  • 录用日期:2020-05-15
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