[1]范震,马开平,姜顺婕,等.基于改进GM（1，N）模型的我国大豆价格影响因素分析及预测研究[J].大豆科学,2016,35(05):847-852.[doi:10.11861/j.issn.1000-9841.2016.05.0847]
　FAN Zhen,MA Kai-ping,JIANG Shun-jie,et al.Influence Factors Analysis and Price Prediction of Soybean in China Based on Improved GM (1, N) Model[J].Soybean Science,2016,35(05):847-852.[doi:10.11861/j.issn.1000-9841.2016.05.0847]
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基于改进GM（1，N）模型的我国大豆价格影响因素分析及预测研究

《大豆科学》[ISSN:1000-9841/CN:23-1227/S] 卷: 第35卷 期数: 2016年05期 页码: 847-852 栏目: 出版日期: 2016-09-20
Title:
Influence Factors Analysis and Price Prediction of Soybean in China Based on Improved GM (1, N) Model
作者:
范震; 马开平; 姜顺婕; 石波
（南京农业大学 工学院，江苏 南京 210031）
Author(s):
FAN Zhen; MA Kai-ping; JIANG Shun-jie; SHI Bo
（College of Engineering, Nanjing Agricultural University, Nanjing 210031, China）
关键词:
大豆价格; 灰色关联分析; 灰色预测; GM（1; N）
Keywords:
Soybean price;  Grey correlation analysis;  Grey prediction;  GM (1;  N)
DOI:
10.11861/j.issn.1000-9841.2016.05.0847
文献标志码:
A
摘要:
大豆是我国重要的粮食作物和油料作物，其价格对于国民经济尤其是农业经济的影响意义深远。大豆价格的稳定对于我国大豆市场的健康发展有着重要的现实意义。在灰色理论的基础上，提出了一种改进GM（1，N）大豆价格预测模型，首先运用灰色关联分析法对我国大豆价格的影响因素进行分析，选择主要的影响因素；再将这些影响因素作为模型的相关因素变量，构建GM（1，N）大豆价格预测模型。采用2010-2015年的大豆数据进行实证研究，模型选取国内大豆自给量、世界大豆产量、国民消费价格指数、消费者信心指数4个变量作为相关因素变量；模型预测误差为2.10%，预测精度较高，能够较好地掌握大豆价格的变化规律，可以为大豆价格市场预测及国家宏观政策的制定提供理论指导。
Abstract:
Soybean is an important food crop and oil crop in China, and its price has a profound impact on the national economy, especially the agricultural economy. The stability of soybean prices for the healthy development of the soybean market in China has important practical significance. Based on the grey theory, an improved GM (1, N) model is proposed. First, using the gray correlation analysis method to analyze the factors that affect the price of soybean in our country, and select the main factors.Then select these factors as the correlation factors of the model, to build the GM (1, N) model. We used the 2010 to 2015 soybean data for empirical research, and the model selected four variables of the domestic soybean self-sufficiency, world soybean production, the country′s consumer price index, consumer confidence index as a related factor. Model prediction error was 2.10% and the prediction accuracy is higher. It could grasp the change of soybean price better, and provide theoretical guidance for the soybean price market forecast and national macro policy formulation.

参考文献/References:

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备注/Memo

基金项目：国家自然科学基金（71101072, 71301077，71401076）；南京农业大学中央高校基本科研业务费人文社会科学基金(SK2016006)。第一作者简介：范震(1990-)，男，硕士，主要从事预测与决策、产品市场扩散、农业系统工程等研究。E-mail: 409652452@qq.com。

更新日期/Last Update: 2016-09-25