SV因子分析框架下的农产品市场短期预测
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摘要
农产品市场价格事关民众生计和社会稳定。近年来中央一号文件和政府工作报告中多次提到要加强农产品市场监测预警工作，避免农产品市场价格大起大落。农产品市场短期价格受多种复杂因素的影响，使得短期波动加剧，市场短期风险凸显，价格预测难度加大。在我国当前市场主体尚不成熟、市场体系尚不健全、法制环境尚不完善等现状下，农业生产经营者由于难以对市场供求和价格变化做出准确预期，时常要面临和承担价格波动所带来的市场风险；农业行政管理部门也常常因缺少有效的市场价格短期走势的预判信息，难以采取有预见性的事前调控措施；消费者由于缺少权威信息的及时引导，在市场价格频繁波动中极易产生恐慌心理，从而加速价格波动的恶性循环。因此，开展农产品市场短期预测研究，对促进农业生产稳定、农民增收和农产品市场有效供给具有重要的理论和现实意义。
本研究选择粮食、畜产品、蔬菜、水果等主要农产品为研究对象，以现代西方经济学、计量经济学、统计学、价格学等有关理论为指导，采用了非参数核密度估计法、H-P滤波法、基于ARCH类模型的风险价值法（VaR）、事件分析法（Event Study）、分位数回归技术、多层前馈神经网络技术、灰色模型法、向量自回归法（VAR）、非平稳时间序列法（SARIMA模型）、季节调整法（CencusX12法）、Holt-Winters法、组合预测法等12种研究方法，深入剖析影响农产品市场价格的强波动类因子（简称S因子）和易波动类因子（简称V因子），研究SV因子交织下的农产品市场价格波动规律，重点突破农产品市场短期预测技术，建立了基于不同信息、不同技术、不同目标的农产品市场价格短期预测模型，初步设计与实现了农产品市场价格智能预测系统。本研究主要开展了以下几个方面的创新性研究：
（1）针对农产品市场价格影响因素复杂多样，提出了SV因子的框架分析法思想，开展了SV因子分析法的理论基础研究。本研究将农产品市场价格影响因子划分为强波动因子和易波动类因子，为异常市场波动早期预警提供了新视角；探讨了预测方法的五个发展阶段、农产品市场预测与相关学科的关系和农产品市场价格波动理论等内容。
（2）开展了SV因子交织下的农产品市场价格波动研究。一是立足国内视角的常规经济因素和非经济性因素，以蔬菜为例的实证研究得出结论：Granger检验表明生产成本（化肥）、流动性过剩、热钱和气候等因素是蔬菜市场价格波动的原因。化肥价格的影响存在一定滞后期，长期协整向量为[1,-1.18]。流动性过剩（货币发行量）对蔬菜价格短期波动影响较为明显，货币发行量增长1%，蔬菜价格将上涨1.29%；其次为气候（气温）对蔬菜价格短期波动的影响，气温下降或上升1%，蔬菜价格将上涨或下跌0.08%；热钱对蔬菜短期价格波动影响很小。二是立足国际视角的农产品市场价格传导关系，以玉米和大豆为例的实证结果表明：Granger检验表明国际市场价格变动是国内市场价格变动的原因，反之则不成立，且国内玉米价格受国际市场的影响要小于国际大豆价格对国内大豆价格的影响；长期波动关系看，国内玉米价格与国际玉米价格的协整向量为[1,-0.61]，国内大豆价格与国际大豆价格的协整向量为[1,-0.86]；短期波动关系看，上一期国际玉米价格上涨1%，本期国内玉米价格将上涨0.07%，而国际大豆市场价格对国内大豆市场价格的影响是同步的，国际大豆当期价格上涨1%，国内大豆价格将上涨0.44%。
（3）采用非参数核密度技术估计了11种蔬菜和12种水果市场收益率的概率密度分布，建立了基于ARCH类模型的农产品市场短期风险动态评估模型。采用核密度技术的估计结果表明：传统意义上的正态分布、Beta分布、Burr分布、Gamma分布等经验分布不是最优的；蔬菜和水果市场收益率的概率密度分布是不对称的，涨价风险要高于降价风险，且大多数蔬菜品种和水果品种的价格大起大落成为常态。以大豆和蔬菜为例，利用GARCH模型分别计算了大豆期货市场收益率的日度VaR和菠菜批发市场收益率的日度VaR、周度VaR、月度VaR，实证分析结果表明：基于GARCH模型计算的风险价值可以较好地反映市场收益率的分布和波动性两个因素，不仅刻画了市场收益率变化的动态过程，适应风险价值的需要，而且在很大程度上也提高了VaR的精度。
（4）开展了基于S因子的农产品市场短期建模研究。针对突发性事件主导下的价格预测，在比较分析经典的事件分析法、基于虚拟变量的事件分析法和基于知识的事件分析法等三类方法的基础上，创新性的提出了突发事件的IPAD预测法框架，将事件分解为影响强度（Intensity）、持续时间（Persistence）、衰减模式（Attenuation model）和冲击方向（Direction）等4个要素。针对市场价格的非线性变化和不确定性，建立了多层前馈人工神经网络预测模型，研究结果表明对猪肉、鸡肉和鸡蛋的预测准确性在95%以上，对蔬菜和水果的预测准确性在90%以上。针对数据样本量小的问题，建立了灰色预测GM(1,1)模型，研究结果表明对猪肉、鸡肉和鸡蛋的预测准确性基本都在95%以上。
（5）开展了基于V因子的农产品市场短期建模研究。在结构化模型研究方面，分别建立了均值回归预测模型和分位数回归预测模型，并对猪肉、鸡肉和鸡蛋等3种农产品进行了实证分析，预测结果表明：基于均值回归模型的猪肉价格预测精度在90%左右，鸡肉和鸡蛋价格的预测精度在98%以上；基于分位数回归预测模型的预测精度都在99%以上。在时序分析模型研究方面，建立了向量误差修正模型（VEC）、SARIMA模型、Holt-Winters季节指数平滑模型和Census X12季节分解法等4类模型，并对猪肉、鸡肉、鸡蛋、蔬菜和水果等5类农产品进行了实证分析，预测结果表明：4类时间序列模型预测精度都在90%以上，其中鸡蛋和鸡肉的模型预测精度在95%以上。
（6）开展了基于过程的模型集成预测和基于预测结果的组合预测研究。由于不同模型对数据信息的提取程度不尽相同，随着时间推移单项预测的稳健性会有所降低，在未来不确定性增多的情况下组合预测往往更具有优势。以蔬菜和水果为例的组合预测研究结果表明：大多数情况下通过组合预测可以提高预测精度，但也存在组合预测效果不如某单一预测方法的样本现象；对预测误差较大的单项预测方法进行组合，预测精度提高的幅度会更大一些，如果进行组合的各种单项预测方法本身的精度都很高，则组合预测精度提高的幅度就很有限。
Agricultural product market price is relevant to the livelihood of the people and social stability. Inrecent years, the Central No.1Document and Government Report had several times mentioned tostrengthen agricultural product market Monitoring and early warning so as to ensure that the agriculturalproduct market price does not fluctuate wildly. The price of agricultural product short-term market isinfluenced by multiple complicated factors, which makes short-term fluctuation drastic, short-termmarket risk highlighted, and the difficulty of price forecast increased. Under the situation in whichmarket main body is not mature, market system is not perfect, and legal system environment still is notperfected, etc., agricultural producers often face and take the market risk brought by price fluctuationdue to the difficulty of precise prediction for market supply-demand and price fluctuation. Departmentsof agricultural administration are often short of effective Anticipation information of market priceshort-term trend, and difficult to adopt forward-looking and beforehand control measures. Due to theshortage of timely authority information guide, the consumers are susceptible to panic psychologyduring frequent market prices fluctuation, which hence speeds up the vicious circle of price fluctuation.Therefore, it is of important theoretical and practical significance for us to carry out short-term forecastof agricultural product market, which will help to promote stable agricultural production, increasefarmers’ income, and guarantee the supply of agricultural product market.
This dissertation has selected pork, chicken, egg, vegetable and fruit as the research object, takenmodern western economics, econometrics, statistics, price theory and other relevant theories as theguide, and adopted12kinds of study methods of non-parameter kernel density estimation, H-P filteringmethod, value at risk based on ARCH Model, event study, quantile regression, multilayer feed-forwardneural network, grey model method, vector auto-regression method, non-stationary time series method(SARIMA Model), season adjustment method (Cencus X12method), Holt-Winters method, andcombination forecasting method. This study has further analyzed the strong fluctuation class factor (Sfactor) and volatile class factor (V factor) that influence agricultural product market price, studied thefluctuation laws of agricultural product market price intertwined by SV factors, focused onbreakthroughs of short-term forecast technology of agricultural product market, constructed short-termprediction models of agricultural product market price based on different information, differenttechnology, and different objectives, and preliminary designed and realized the intelligent forecastingsystem of agricultural product market price. This study has mainly unfolded innovative studies in thefollowing aspects:
(1) To counter the complicated and diversified factors influencing agricultural product market price,this study had put forward the thoughts of framework analysis based on SV factors, and implementedstudies on basic theory of SV factors. This study has broken down the factors influencing agriculturalproduct market price into strong fluctuation class factors and volatile class factors, which has provided anew viewing angle for early warning of abnormal market price fluctuation. In addition, it also exploredthe5development phases of forecasting methods, the relations between agricultural product market forecast and relevant disciplined, as well as fluctuation theories of agricultural product market price andother contents.
(2) We have done some research on price fluctuation of agricultural product market interwoven bySV factors. Based on the conventional economic factors and non-economic factors from the perspectiveof China, we have reached the conclusion taking vegetable as the empirical study: it shows productioncost (chemical fertilizer), excess liquidity, hot money and climate are the reasons for vegetable marketprice fluctuation by Granger test. The impacts of chemical fertilizer has certain lag phase and thelong-term co-integration vector is [1,-1.18]. The short-term impact of excess liquidity (amount ofcurrency issue) on vegetable price is fairly significant. When the amount of currency issue increases1%,vegetable price rises by1.29%. Second is the impact of climate (temperature) on vegetable priceshort-term fluctuation. When temperature declines or rises by1%, the price of vegetable will rise ordecline0.08%. The short-term impact of hot money on vegetable price is the minimum. Based on thetransmission relationship from the perspective of agricultural product prices of international market,taking corn and soybeans as an example the empirical study result shows: international market pricechange is the reason for domestic market price change, not vice versa by Granger test. The impact ofinternational corn price on domestic corn price is less than that of international soybean price ondomestic soybean price. Viewing the long-term fluctuation relation, the co-integrated vector of domesticcorn price and international corn price is [1,-0.61] and the co-integrated vector of domestic soybeanprice and international soybean price is [1,-0.86]. Viewing the short-term fluctuation relation, the cornprice of prophase rose by1%while domestic corn price of this phase rises0.07%. However, the impactof international soybean market price on domestic soybean market price is synchronous. Wheninternational soybean price of this phase rises1%, the domestic soybean price will rise0.44%.
(3) We have adopted non-parameter kernel density technology to fit the distribution of probabilitydensity function (PDF) of11vegetables and12fruits market returns, and constructed the dynamicassessment model of agricultural product market short-term risk base on ARCH model. The results ofkernel density estimation show: the normal distribution, Beta distribution, Burr distribution, Gammadistribution and other empirical distribution are not the best. The probability density distribution ofvegetable and fruit market returns is asymmetric. The risk of price rising is greater than that of pricedeclining, and the phenomenon of prices wildly fluctuation of vegetable and fruit becomes norm.Taking soybean and vegetable as an example, we have used GARCH model to calculate the daily VaR,weekly VaR and monthly VaR of the market returns. The results of empirical approach show: VaRbased on GARCH model can fairly well reflect the distribution and fluctuation of market returns. It cannot only describe the dynamic course of market returns changes, getting adapted to the needs of VaR,but also to a large extent improve the precision of VaR.
(4) We have unfolded short term model construction research on agricultural product market basedon S factors. To counter price forecast guided by unexpected events, on the basis of Comparison ofclassic events study method, event study based on dummy variable and event study based on knowledge,we have innovatively put forward IPAD forecasting framework of unexpected events and divided events into4essential elements of intensity, persistence, attenuation model, and impact direction. Tocounter nonlinear change and uncertainty of market price, we have constructed multilayer feed-forwardneural network model. The results show that the precision of pork, chicken and egg forecast is over95%,and the precision of vegetables and fruits forecast is over90%. To counter the problem of small datasample, we have constructed grey forecasting model GM (1,1). The results show that the forecastprecision of pork, chicken meat and egg basically all over95%.
(5) We have unfolded short-term model construction research on agricultural product market basedon V factors. In the aspect of structural model, we have constructed the mean regression forecast modeland the quantile regression forecast model respectively, and conducted empirical analysis of pork,chicken and egg. The forecasting results show that the precision for pork price forecast is about90%and the precision for chicken and egg price forecasts is over98%based on mean regression model, andthe precision of quantile regression forecast model is over99%. In the aspect of time series model, wehave constructed the vector error correction model (VEC), SARIMA model, Holt-Winters seasonexponential smoothing model and Census X12season decomposition method. We have undertakenempirical forecasts of five kinds of agricultural products including pork, chicken meat, egg, vegetableand fruit. The results show that the precision of4kinds of time series models are all over90%, of which,the precision of egg and chicken model is over95%.
(6) We have unfolded combination forecasts based on forecasting course and forecasting resultsrespectively. Different models have different extraction degree of information. As time goes on theprecision of individual forecast will be not robust, under the situation of increased uncertainty in thefuture, combination forecast often has more advantages. The results of combination forecasts takingvegetable and fruit as an example show that combination forecasts can improve prediction accuracy inmost cases. However, there are also some special exceptions, i.e. combination forecasting result is notas good as single method. When we undertake combination forecasts based on single method that hasgreater forecasting error, the improvement of prediction precision will be greater. If the precision ofindividual methods is very high, the improvement of precision for combined forecasts will be verylimited.
本研究选择粮食、畜产品、蔬菜、水果等主要农产品为研究对象，以现代西方经济学、计量经济学、统计学、价格学等有关理论为指导，采用了非参数核密度估计法、H-P滤波法、基于ARCH类模型的风险价值法（VaR）、事件分析法（Event Study）、分位数回归技术、多层前馈神经网络技术、灰色模型法、向量自回归法（VAR）、非平稳时间序列法（SARIMA模型）、季节调整法（CencusX12法）、Holt-Winters法、组合预测法等12种研究方法，深入剖析影响农产品市场价格的强波动类因子（简称S因子）和易波动类因子（简称V因子），研究SV因子交织下的农产品市场价格波动规律，重点突破农产品市场短期预测技术，建立了基于不同信息、不同技术、不同目标的农产品市场价格短期预测模型，初步设计与实现了农产品市场价格智能预测系统。本研究主要开展了以下几个方面的创新性研究：
（1）针对农产品市场价格影响因素复杂多样，提出了SV因子的框架分析法思想，开展了SV因子分析法的理论基础研究。本研究将农产品市场价格影响因子划分为强波动因子和易波动类因子，为异常市场波动早期预警提供了新视角；探讨了预测方法的五个发展阶段、农产品市场预测与相关学科的关系和农产品市场价格波动理论等内容。
（2）开展了SV因子交织下的农产品市场价格波动研究。一是立足国内视角的常规经济因素和非经济性因素，以蔬菜为例的实证研究得出结论：Granger检验表明生产成本（化肥）、流动性过剩、热钱和气候等因素是蔬菜市场价格波动的原因。化肥价格的影响存在一定滞后期，长期协整向量为[1,-1.18]。流动性过剩（货币发行量）对蔬菜价格短期波动影响较为明显，货币发行量增长1%，蔬菜价格将上涨1.29%；其次为气候（气温）对蔬菜价格短期波动的影响，气温下降或上升1%，蔬菜价格将上涨或下跌0.08%；热钱对蔬菜短期价格波动影响很小。二是立足国际视角的农产品市场价格传导关系，以玉米和大豆为例的实证结果表明：Granger检验表明国际市场价格变动是国内市场价格变动的原因，反之则不成立，且国内玉米价格受国际市场的影响要小于国际大豆价格对国内大豆价格的影响；长期波动关系看，国内玉米价格与国际玉米价格的协整向量为[1,-0.61]，国内大豆价格与国际大豆价格的协整向量为[1,-0.86]；短期波动关系看，上一期国际玉米价格上涨1%，本期国内玉米价格将上涨0.07%，而国际大豆市场价格对国内大豆市场价格的影响是同步的，国际大豆当期价格上涨1%，国内大豆价格将上涨0.44%。
（3）采用非参数核密度技术估计了11种蔬菜和12种水果市场收益率的概率密度分布，建立了基于ARCH类模型的农产品市场短期风险动态评估模型。采用核密度技术的估计结果表明：传统意义上的正态分布、Beta分布、Burr分布、Gamma分布等经验分布不是最优的；蔬菜和水果市场收益率的概率密度分布是不对称的，涨价风险要高于降价风险，且大多数蔬菜品种和水果品种的价格大起大落成为常态。以大豆和蔬菜为例，利用GARCH模型分别计算了大豆期货市场收益率的日度VaR和菠菜批发市场收益率的日度VaR、周度VaR、月度VaR，实证分析结果表明：基于GARCH模型计算的风险价值可以较好地反映市场收益率的分布和波动性两个因素，不仅刻画了市场收益率变化的动态过程，适应风险价值的需要，而且在很大程度上也提高了VaR的精度。
（4）开展了基于S因子的农产品市场短期建模研究。针对突发性事件主导下的价格预测，在比较分析经典的事件分析法、基于虚拟变量的事件分析法和基于知识的事件分析法等三类方法的基础上，创新性的提出了突发事件的IPAD预测法框架，将事件分解为影响强度（Intensity）、持续时间（Persistence）、衰减模式（Attenuation model）和冲击方向（Direction）等4个要素。针对市场价格的非线性变化和不确定性，建立了多层前馈人工神经网络预测模型，研究结果表明对猪肉、鸡肉和鸡蛋的预测准确性在95%以上，对蔬菜和水果的预测准确性在90%以上。针对数据样本量小的问题，建立了灰色预测GM(1,1)模型，研究结果表明对猪肉、鸡肉和鸡蛋的预测准确性基本都在95%以上。
（5）开展了基于V因子的农产品市场短期建模研究。在结构化模型研究方面，分别建立了均值回归预测模型和分位数回归预测模型，并对猪肉、鸡肉和鸡蛋等3种农产品进行了实证分析，预测结果表明：基于均值回归模型的猪肉价格预测精度在90%左右，鸡肉和鸡蛋价格的预测精度在98%以上；基于分位数回归预测模型的预测精度都在99%以上。在时序分析模型研究方面，建立了向量误差修正模型（VEC）、SARIMA模型、Holt-Winters季节指数平滑模型和Census X12季节分解法等4类模型，并对猪肉、鸡肉、鸡蛋、蔬菜和水果等5类农产品进行了实证分析，预测结果表明：4类时间序列模型预测精度都在90%以上，其中鸡蛋和鸡肉的模型预测精度在95%以上。
（6）开展了基于过程的模型集成预测和基于预测结果的组合预测研究。由于不同模型对数据信息的提取程度不尽相同，随着时间推移单项预测的稳健性会有所降低，在未来不确定性增多的情况下组合预测往往更具有优势。以蔬菜和水果为例的组合预测研究结果表明：大多数情况下通过组合预测可以提高预测精度，但也存在组合预测效果不如某单一预测方法的样本现象；对预测误差较大的单项预测方法进行组合，预测精度提高的幅度会更大一些，如果进行组合的各种单项预测方法本身的精度都很高，则组合预测精度提高的幅度就很有限。
Agricultural product market price is relevant to the livelihood of the people and social stability. Inrecent years, the Central No.1Document and Government Report had several times mentioned tostrengthen agricultural product market Monitoring and early warning so as to ensure that the agriculturalproduct market price does not fluctuate wildly. The price of agricultural product short-term market isinfluenced by multiple complicated factors, which makes short-term fluctuation drastic, short-termmarket risk highlighted, and the difficulty of price forecast increased. Under the situation in whichmarket main body is not mature, market system is not perfect, and legal system environment still is notperfected, etc., agricultural producers often face and take the market risk brought by price fluctuationdue to the difficulty of precise prediction for market supply-demand and price fluctuation. Departmentsof agricultural administration are often short of effective Anticipation information of market priceshort-term trend, and difficult to adopt forward-looking and beforehand control measures. Due to theshortage of timely authority information guide, the consumers are susceptible to panic psychologyduring frequent market prices fluctuation, which hence speeds up the vicious circle of price fluctuation.Therefore, it is of important theoretical and practical significance for us to carry out short-term forecastof agricultural product market, which will help to promote stable agricultural production, increasefarmers’ income, and guarantee the supply of agricultural product market.
This dissertation has selected pork, chicken, egg, vegetable and fruit as the research object, takenmodern western economics, econometrics, statistics, price theory and other relevant theories as theguide, and adopted12kinds of study methods of non-parameter kernel density estimation, H-P filteringmethod, value at risk based on ARCH Model, event study, quantile regression, multilayer feed-forwardneural network, grey model method, vector auto-regression method, non-stationary time series method(SARIMA Model), season adjustment method (Cencus X12method), Holt-Winters method, andcombination forecasting method. This study has further analyzed the strong fluctuation class factor (Sfactor) and volatile class factor (V factor) that influence agricultural product market price, studied thefluctuation laws of agricultural product market price intertwined by SV factors, focused onbreakthroughs of short-term forecast technology of agricultural product market, constructed short-termprediction models of agricultural product market price based on different information, differenttechnology, and different objectives, and preliminary designed and realized the intelligent forecastingsystem of agricultural product market price. This study has mainly unfolded innovative studies in thefollowing aspects:
(1) To counter the complicated and diversified factors influencing agricultural product market price,this study had put forward the thoughts of framework analysis based on SV factors, and implementedstudies on basic theory of SV factors. This study has broken down the factors influencing agriculturalproduct market price into strong fluctuation class factors and volatile class factors, which has provided anew viewing angle for early warning of abnormal market price fluctuation. In addition, it also exploredthe5development phases of forecasting methods, the relations between agricultural product market forecast and relevant disciplined, as well as fluctuation theories of agricultural product market price andother contents.
(2) We have done some research on price fluctuation of agricultural product market interwoven bySV factors. Based on the conventional economic factors and non-economic factors from the perspectiveof China, we have reached the conclusion taking vegetable as the empirical study: it shows productioncost (chemical fertilizer), excess liquidity, hot money and climate are the reasons for vegetable marketprice fluctuation by Granger test. The impacts of chemical fertilizer has certain lag phase and thelong-term co-integration vector is [1,-1.18]. The short-term impact of excess liquidity (amount ofcurrency issue) on vegetable price is fairly significant. When the amount of currency issue increases1%,vegetable price rises by1.29%. Second is the impact of climate (temperature) on vegetable priceshort-term fluctuation. When temperature declines or rises by1%, the price of vegetable will rise ordecline0.08%. The short-term impact of hot money on vegetable price is the minimum. Based on thetransmission relationship from the perspective of agricultural product prices of international market,taking corn and soybeans as an example the empirical study result shows: international market pricechange is the reason for domestic market price change, not vice versa by Granger test. The impact ofinternational corn price on domestic corn price is less than that of international soybean price ondomestic soybean price. Viewing the long-term fluctuation relation, the co-integrated vector of domesticcorn price and international corn price is [1,-0.61] and the co-integrated vector of domestic soybeanprice and international soybean price is [1,-0.86]. Viewing the short-term fluctuation relation, the cornprice of prophase rose by1%while domestic corn price of this phase rises0.07%. However, the impactof international soybean market price on domestic soybean market price is synchronous. Wheninternational soybean price of this phase rises1%, the domestic soybean price will rise0.44%.
(3) We have adopted non-parameter kernel density technology to fit the distribution of probabilitydensity function (PDF) of11vegetables and12fruits market returns, and constructed the dynamicassessment model of agricultural product market short-term risk base on ARCH model. The results ofkernel density estimation show: the normal distribution, Beta distribution, Burr distribution, Gammadistribution and other empirical distribution are not the best. The probability density distribution ofvegetable and fruit market returns is asymmetric. The risk of price rising is greater than that of pricedeclining, and the phenomenon of prices wildly fluctuation of vegetable and fruit becomes norm.Taking soybean and vegetable as an example, we have used GARCH model to calculate the daily VaR,weekly VaR and monthly VaR of the market returns. The results of empirical approach show: VaRbased on GARCH model can fairly well reflect the distribution and fluctuation of market returns. It cannot only describe the dynamic course of market returns changes, getting adapted to the needs of VaR,but also to a large extent improve the precision of VaR.
(4) We have unfolded short term model construction research on agricultural product market basedon S factors. To counter price forecast guided by unexpected events, on the basis of Comparison ofclassic events study method, event study based on dummy variable and event study based on knowledge,we have innovatively put forward IPAD forecasting framework of unexpected events and divided events into4essential elements of intensity, persistence, attenuation model, and impact direction. Tocounter nonlinear change and uncertainty of market price, we have constructed multilayer feed-forwardneural network model. The results show that the precision of pork, chicken and egg forecast is over95%,and the precision of vegetables and fruits forecast is over90%. To counter the problem of small datasample, we have constructed grey forecasting model GM (1,1). The results show that the forecastprecision of pork, chicken meat and egg basically all over95%.
(5) We have unfolded short-term model construction research on agricultural product market basedon V factors. In the aspect of structural model, we have constructed the mean regression forecast modeland the quantile regression forecast model respectively, and conducted empirical analysis of pork,chicken and egg. The forecasting results show that the precision for pork price forecast is about90%and the precision for chicken and egg price forecasts is over98%based on mean regression model, andthe precision of quantile regression forecast model is over99%. In the aspect of time series model, wehave constructed the vector error correction model (VEC), SARIMA model, Holt-Winters seasonexponential smoothing model and Census X12season decomposition method. We have undertakenempirical forecasts of five kinds of agricultural products including pork, chicken meat, egg, vegetableand fruit. The results show that the precision of4kinds of time series models are all over90%, of which,the precision of egg and chicken model is over95%.
(6) We have unfolded combination forecasts based on forecasting course and forecasting resultsrespectively. Different models have different extraction degree of information. As time goes on theprecision of individual forecast will be not robust, under the situation of increased uncertainty in thefuture, combination forecast often has more advantages. The results of combination forecasts takingvegetable and fruit as an example show that combination forecasts can improve prediction accuracy inmost cases. However, there are also some special exceptions, i.e. combination forecasting result is notas good as single method. When we undertake combination forecasts based on single method that hasgreater forecasting error, the improvement of prediction precision will be greater. If the precision ofindividual methods is very high, the improvement of precision for combined forecasts will be verylimited.
引文
[1]曹聪梅，甘仞初.非平稳时间序列预测模型在农产品供求预测量中的应用研究.中国管理科学，2003,11（10）：320-323.
[2]曹志广，王安兴，杨军敏.股票收益率非正态性的蒙特卡罗模拟检验[J].财经研究，2005，31（10）：34-41.
[3]陈启欢.中国股票市场收益率分布曲线的实证[J].数理统计与管理，2002，21(5)：9-11.
[4]陈诗一著.非参数支持向量回归和分类理论及其在金融市场预测中的应用[M].北京：北京大学出版社，2008：9-18.
[5]陈思杰，周浩.电力市场电价预测综述[J].继电器，2006（11）：54-60.
[6]陈永福.中国粮食供求预测与对策探讨[J].农业经济问题，2005（4）：8-13.
[7]陈友华，侯定丕.基于预测有效度的优性组合预测模型研究[J].中国科技大学学报，2002，32（2）：172-180.
[8]陈友华.广义加权算术平均组合预测法的最优化理论基础及性质[J].系统工程理论与实践，2003，（4）：37-41.
[9]程国强,徐雪高.改革开放以来我国农产品价格波动的回顾[J].重庆工学院学报:社会科学，2009，23(4)：1-3.
[10]程国强.理性应对粮食价格波动[J].农经观察，2010：8-10.
[11]程国强.粮价异常波动亟须综合调控[J].中国发展观察，2010（6）：5-6.
[12]程瑞芳.我国农产品价格形成机制及波动效应分析[J].中国流通经济，2007，21（3）：22-24.
[13]程贤禄.北京农产品批发市场蔬菜价格预测预报体系研究[J].北京农业科学，2002（2）：1-10.
[14]邓聚龙.灰色系统理论教程[M].武汉：华中理工大学出版社，1990：25-30.
[15]东梅，赵俊杰.市场分形理论与2009年中国小麦价格预测[J].乡镇经济，2009（9）：16-18.
[16]董晓霞，李干琼，刘自杰.农产品市场价格短期预测方法的选择及应用—以鲜奶零售价格为例[J].山东农业科学，2010（1）：109-113.
[17]董晓霞，刘自杰，李志强.我国鲜奶零售价格波动规律及影响因素分析[J].价格月刊，2010,398（7）：6-10.
[18]范振龙.转配股上市对我国股市影响的事件研究[J].复旦学报：自然科学版.2001（2）：220-227.
[19]封建强,王福新.中国股市收益率分布函数研究[J].中国管理科学，2003，11（1）：14-21.
[20]冯文权编著.经济预测与决策技术（第五版）[M].武汉：武汉大学出版社，2008：39-44.
[21]傅如南，林丕源，严尚维，等.基于ARIMA的肉鸡价格预测建模与应用[J].中国畜牧杂志，2008（20）：17-21
[22]高铁梅主编.计量经济分析方法与建模[M].北京：清华大学出版社，2009：295-300.
[23]郭海燕，田新时.极值理论在风险度量中的应用基于上证指数[J].运筹与管理，2004，13（1）：106-111.
[24]郝勇，范军晖编著.系统工程方法与应用[M].北京：科学出版社，2007：62-64.
[25]何启志.国际农产品价格波动风险研究[J].财贸研究，2010，21（5）：63-69.
[26]胡冰川.消费价格指数、农产品价格与货币政策[J].中国农村经济，2010，（12）：37-45.
[27]胡涛.基于非参数的水产品价格预测系统研究[博士论文].北京：中国农业大学经济管理学院.2005.
[28]黄季焜,李宁辉.中国农业政策分析和预测模型——CAPSiM[J].南京农业大学学报（社会科学版），2003，3（2）：30-41.
[29]黄季焜.从农业政策干预程度看中国农产品市场与全球市场的整合[J].世界经济，2008（4）：3-10.
[30]黄季焜.农产品供求视角下农业经济和政策前沿问题研究[J].经济调查，2010（3）：1-7.
[31]黄岩，张国春，王其藩、朱道立.一种新的计算组合预测权重的方法[J].管理工程学报，2001，15（2）：44-46.
[32]黄莹，刘海龙.基于动态VaR模型的我国银行间拆借市场风险度量研究[J].上海管理科学，2008（1）：51-54.
[33]蒋白懿，代进，高金良.城市日用水量预测模型比较研究[J].沈阳建筑大学学报:自然科学版，2008（2）：278-281.
[34]李晖.粮食价格波动与政府调控[J].农村经济，2004（11）：39-40.
[35]李干琼，许世卫，李哲敏，董晓霞.基于非参数核密度估计的中国水果市场收益率分布研究[J].统计与决策，2012（7）：97-100.
[36]李干琼，许世卫，孙益国，李哲敏，董晓霞.中国蔬菜市场价格短期波动与风险评估[J].中国农业科学，2011，44（7）：1502-1511.
[37]李干琼，许世卫，李哲敏，董晓霞.农产品市场价格短期预测方法与模型研究——基于时间序列模型的预测[J].中国农业大学学报，2011，16（2）：172-178.
[38]李干琼，董晓霞，王启现.加拿大种业管理做法[J].世界农业，2010（9）：17-20.
[39]李干琼，许世卫，李哲敏，董晓霞.农产品市场价格超短期预测研究——基于西红柿日批发价格的现代时间序列法建模[J].华中农业大学学报（社科版），2010（6）：40-45.
[40]李干琼，李哲敏.2009年禽蛋市场价格回顾及展望[J].中国食物与营养.2010（2）：50-53.
[41]李国祥.2003年以来我国农产品价格轮番上涨的经济学分析.新视野，2011（1）：19-21.
[42]李国柱，刘德智主编.计量经济学试验教程[M].北京：中国经济出版社，2009：193-220.
[43]李哲敏，李干琼.禽蛋市场价格短期预测[J].中国食物与营养，2010（6）：36-40.
[44]李哲敏，李干琼.禽蛋市场价格及其短期预测因子分析[J].中国食物与营养，2009（12）：36-39.
[45]李哲敏，许世卫，董晓霞，等.中国禽蛋产业链短期市场价格传导机制[J].中国农业科学，2010，43（23）：4951-4962.
[46]李志强.农产品市场分析重点与关键技术[J].农业展望，2010（1）：53-58.
[47]梁来存.我国粮食单产保险纯费率厘定的实证研究[J].统计研究，2010，27（5）：67-73.
[48]廖永松，黄季焜.21世纪我国粮食安全保障与灌溉水需求预测[J].中国水利，2004（1）：36-38.
[49]刘春芳,王济民.我国生猪价格波动原因分析及趋势判断[J].农业展望，2009，5（5）：12-14.
[50]刘春海等.粮食价格预测方法探讨[J].农业发展与金融，2007（6）：84.
[51]刘峰，王儒敬，李传席.ARIMA模型在农产品价格预测中的应用[J].计算机工程与应用，2009，45（25）：238-239.
[52]刘国琴.山东省大葱和大蒜价格波动原因及对策[J].中国蔬菜，2008（10）：4-7.
[53]刘海清,方佳.基于指数平滑模型的海南省芒果价格预测[J].热带农业科学，2010（1）：79-81.
[54]刘洪宇.商品市场价格的三时点预测模型及其应用[J].预测，2001（3）：51-54
[55]刘家富，周慧秋.国内大豆市场价格波动及其影响因素分析[J].东北农业大学学报(社会科学版)，2010，8（4）：10-13.
[56]罗万纯，陈永福.中国大米市场价格共因子分析.中国农村观察，2007（4）：2-12.
[57]吕新业，王济民，吕向东.我国食物安全的短期预测与预警研究[J].农业经济问题，2006，(5)：49-55.
[58]马超群，李红权.风险价值的完全参数方法及其在金融市场风险管理中的应用[J].系统工程理论与实践，2001，21（4）：74-79.
[59]马孝斌，王婷，董霞，等.向量自回归法在生猪价格预测中的应用[J].中国畜牧杂志，2007（23）：4-6
[60]马永开，唐小我，杨桂元.优性组合预测方法存在判别定理[J].预测，1995，（2）：57-58.
[61]马永开，唐小我.多目标组合预测优化模型研究[J].统计研究，1997，（4）：45-48.
[62]马永开，杨桂元，唐小我.非负权重组合预测的冗余定理[J].系统工程理论方法应用，1995，4（4）：33-39.
[63]孟凡新,董彭滔，林小兰.粮食市场价格波动规律、原因及对策分析[J].价格理论与实践，2008（9）：36-37.
[64]宁攸凉.中国生猪价格波动的影响与成因探究[J].畜牧经济，2010，46（2）：52-56.
[65]农业部市场与经济信息司.2009中国农产品市场分析报告[M].北京：中国农业出版社，2010：48-58.
[66]农业部市场与经济信息司.2011中国农产品市场分析报告[M].北京：中国农业出版社，2011：12-50.
[67]农业部市场与经济信息司.农产品市场监测预警工作手册[M].北京：中国农业出本社，2010：100-134.
[68]攀登，邹炎.内幕交易与市场操纵的事件研究[J].当代经济管理，2005（10）：129-136.
[69]平平，刘大有，杨博.组合预测模型在猪肉价格预测中的应用研究[J].计算机工程与科学，2010（5）：109-112.
[70]秦富.河南小麦产业链各环节成本收益研究.农业经济问题，2008（5）：13-19.
[71]沈巍.股票价格预测模型研究[J].财经问题研究，2009（7）：89-93.
[72]沈小燕，杨旭辉.农产品期货价格波动性分析[J].上海第二工业大学学报，2006，23（2）：117-121.
[73]孙见荆.组合预测参数估计方法研究[J].预测，1996（6）：54-56.
[74]孙敬水主编.中级计量经济学[M].上海：上海财经大学出本社，2009：272-318
[75]谭英平.非参数密度估计在个体损失分布中的应用[J].统计研究，2003（8）：40-44.
[76]唐林俊，杨虎.沪深股市收益率分布特征的统计分析[J].数理统计与管理，2004，23（5）：1-4.
[77]唐小我，曹长修，金德运.组合预测最优加权系数向量的进一步研究[J].预测，1994，（2）：48-49.
[78]唐小我，曾勇.组合预测误差校正模型的应用分析[J].管理科学学报，2002，5（6）：53-64.
[79]唐勇，寇贵明.分位数回归视角下的金融市场风险度量研究进展[J].福州大学学报：哲学社会科学版，2009，23（6）：39-44.
[80]滕素珍，刘羽，于建坤.价格预测模型及算法[J].大连理工大学学报，1995(10）：593-595.
[81]田宏伟，詹原瑞.极值理论（EVT）方法用于风险价值（VaR）计算的实证比较与分析[J].系统工程理论与实践，2000（10）：27-35.
[82]汪寿阳，余乐安，房勇，等著.国际油价波动分析与预测[M].湖南：湖南大学出版社，2008：53-64.
[83]汪寿阳.金融危机对全球大宗商品和中国经济的影响[J].中国禽业导刊，2008，25（6）：21-22.
[84]汪同三，王丽.中国计量经济学发展之路.科技促进发展，2010（5）：7-12.
[85]汪同三，张涛等著.组合预测：理论、方法及应用.北京：社会科学文献出版社，2008:58-85.
[86]王川.基于VaR的我国粮食期货市场基差风险度量与分析[J].农村经济与科技，2010（7）：8-11.
[87]王川.我国粮食市场价格的影响因素分析[J].农业经济，2010（7）：24-27.
[88]王吉恒，王新利.农产品市场风险与市场预测研究[J].农业技术经济，2003（3）：1-5.
[89]王丽红，杨汭华，田志宏，等.非参数核密度法厘定玉米区域产量保险费率研究——以河北安国市为例[J].中国农业大学学报，2007，12（1）：90-94.
[90]王明涛.预测方法的有效性分析[J].预测，1994，（6）：57-59.
[91]王强，秦泰增.上证指数的短期预测模型[J].电脑编程技巧与维护，2008（11）：14-15.
[92]王舒鸿.灰色预测模型在鸡蛋价格预测中的应用[J].中国禽业导刊，2008（15）：48-50.
[93]王怡，周应恒，赵文，等.中国苹果市场整合程度及价格波动规律研究[J].南京农业大学学报，2008，31(1)：112-117.
[94]王勇，张浩.小麦期货价格预测的马尔科夫模型[J].安徽农业科学，2008，36（5）：1721
[95]魏振香，李伟娟，李南.影响我国小麦价格变动的因素分析及对策建议[J].分析预测，2010：50-52.
[96]武拉平.农产品地区差价和地区间价格波动规律研究——以小麦、玉米和生猪市场为例[J].农业经济问题，2000（10）：54-58.
[97]肖智，李春红，李潆兵，等.遗传算法在经济混沌组合预测中的应用[J].重庆大学学报，2002，25（11）：143-146.
[98]徐枫.股票价格预测的GARCH模型[J].统计与决策，2006（18）：107-109.
[99]徐国翔.统计预测和决策[M].2版.上海：上海财经大学出版社，2005：261-268
[100]徐雪高.新一轮农产品价格波动周期:特征、机理及影响[J].财经研究，2008，34（8）：110-119.
[101]徐雪高.猪肉价格高位大涨的原因及对宏观经济的影响[J].农业技术经济，2008（3）：4-9.
[102]许世卫，李哲敏，董晓霞，等.中国农产品在产销间价格传导机制研究[J].资源科学，2010，32（11）：2092-2099.
[103]许世卫，李哲敏，李干琼，等.农产品市场价格短期预测研究进展[J].中国农业科学，2011，44（17）：3666-3675.
[104]许世卫.食物安全中的溃堤效应[J].中国食物与营养，2010（8）：8-13.
[105]严广乐，张宁，刘媛华编.系统工程[M].北京：机械工业出版社，2008：227-232
[106]严荣光，严斌剑.对生猪价格波动的探究与思考——基于湖北省2007年以来相关价格的实证分析[J].价格理论与实践，2010（5）：54-55.
[107]杨军，黄季焜，仇焕广，等.国外农产品价格变化对国内价格的影响[J].中国金融，2011，（22）：62-63.
[108]杨奎河.短期电力负荷的智能化预测方法研究[博士论文].陕西：西安电子科技大学计算机学院，2004：7-16.
[109]杨青，曹明，蔡天晔.CVaR-EVT和BMM在极端金融风险管理中的应用研究[J].统计研究，2010，27（6）：78-86.
[110]姚霞，彭汉艮，朱艳，等.时鲜农产品价格预测的ARIMA时序模型构建与运用[J]，农业系统科学与综合研究，2007（1）：89-94.
[111]姚霞，张卫健，朱艳，等.江苏省不同类型蔬菜价格波动的时空特征分析[J].江苏农业科学，2006（5）：190-193.
[112]易丹辉主编.数据分析与Eviews应用[M].北京：中国人民大学出版社，2008：98-103.
[113]喻国平.事件研究：经济和金融研究的重要方法[J].金融与经济.1998（10）：14-15.
[114]曾勇，李玉东，唐小我.简单平均组合预测有效性的应用分析[J].电子科技大学学报，1999，28（1）：84-88.
[115]曾勇，唐小我，曹长修.非负权重最优组合预测方法研究[J].管理工程学报，1995，9（3）：153-161.
[116]曾勇，唐小我，王竹.无偏组合预测模型研究[J].预测，1995（6）：42-45.
[117]曾勇，唐小我.线性规划在非负权重最优组合预测计算中的应用[J].预测，1994（3）：55-56.
[118]张德丰等编著.MATLAB神经网络应用设计[M].北京：机械工业出版社，2009：7-9.
[119]张合成.涉农网络舆情走势与应对[J].新闻战线，2010（4）：66-68.
[120]张化中主编.价格监测及预测预警[M].北京：中国市场出版社，2006：201-218
[121]张慧玲,我国生猪价格周期性波动的规律及其成因分析[J].猪业经济，2010（1）：26-27.
[122]张峭，王川，王克.我国畜产品市场价格风险度量与分析[J].经济问题，2010（3）：90-94.
[123]张峭.中国粮食生产短期预测模型研究[J].农业展望，2006，2（2）：9-12.
[124]张雯丽,李秉龙.我国棉花短期价格波动研究——基于时间序列[J].技术经济，2009，28（4）：88-93.
[125]张小栓.水产品价格预测支持系统研究[博士论文].北京：中国农业大学经济管理学院，2003：1-13.
[126]张兴旺.关于我国农产品市场问题的几点认识与思考[J].农业经济问题，2009（1）：66-69.
[127]张兴旺.我国农业信息分析工作的意义、问题与方向[J].农业图书情报学刊.2008，20（，12）：8-10.
[128]郑小平，高金吉，刘梦婷著.事故预测理论与方法[M].北京：清华大学出版社，2009，129-144.
[129]周传世，刘永清，邹自德.组合预测模型的筛选[J].预测，1995（1）：56-59.
[130]周传世，刘永清.变权重组合预测模型的研究[J].预测，1995（4）：47-57.
[131]周传世，罗国民.加权几何平均组合预测模型及其应用[J].数理统计与管理，1995（2）：17-19
[132]周传世.非线性组合预测及其最优权的确定[J].预测，1994（2）：60-64.
[133]周明磊.事件对国际石油价格影响的时间序列分析[J].数学的认识与实践，2004（8）：12-18.
[134]周宗放，杨春德.组合预测权系数的目标规划确定法[J].系统工程理论方法应用，1995，4（1）：50-55.
[135]朱宏泉，卢祖帝，汪寿阳.Value at risk的核估计理论[J].系统科学与数学，2002，22（3）：365-374.
[136]朱凯，王正林编著.精通MATLAB神经网络[M].北京：电子工业出版社，2010：193-198.
[137]祝金荣著.石油期货价格预测研究[M].北京：冶金工业出本社，2008：83-95.
[138] Abrevaya J., Dahl C.M. The effects of birth inputs on birthweight: evidence from quantileestimation on panel data [J]. Journal of Business&Economic Statistics,2008,(4):379-397.
[139] Al-Qassam M.S., Lane J.A., Forecasting exponential autoregressive models of order1[J].Journal of Time Series Analysis,1989,10:95-113.
[140] Ames W. Taylor. Using Exponentially Weighted Quantile Regression to Estimate Value atRisk and Expected Shortfall [J]. Journal of Financial Econometrics,2008,(6):382-406.
[141] Andrews M.J., Minford P., Peel D., Forecast Encompassing Tests of the Liverpool Model [J].Oxford Bulletin of Economics and Statistics,1995,61:221-256.
[142] Armstrong J.S., et al. Commentary on the Makridakis Time Series Competition(M-competition)[J]. Journal of Forecasting,1983,2:259-311.
[143] Ashton A.H., Ashton R.H. Aggregating Subjective Forecasts: Some Empirical Results [J]Management Science,1985,31(12):1499-1508.
[144] Ashu Jain, Ashish Kumar Varshney, Umesh Chandra Joshi. Short-Term Water DemandForecast Modeling at IIT Kanpur Using Artificial Neural Networks [J]. Water ResourcesManagement,2001,15:299-321.
[145] Banachewicz K., Lucas A., Quantile forecasting for credit risk management using possiblymisspecified hidden markov models [J].Journal of Forecasting,2008,27(7):566-586.
[146] Batchelor R., Dua P., Forecaster Diversity and the Benefits of Combining Forecastes [J]Management Science,1995,41(1):68-75.
[147] Bates J.M, Granger C.W.J., The Combination of Forecasts [J]. Operational ResearchQuarterly,1969,20(4):451-468.
[148] Bollerslev T., Chou R.S., Kroner K.F., ARCH modeling in finance-A review of the theory andempirical evidence. Journal of Econometrics,1992,52:5-59.
[149] Bopp A.E. On Combining Forecasts: Some Extensions and Results [J]. Management Science,1985,31(12).
[150] Bosland C.C. Forecasting the Price of Wheat [J].Journal of the American StatisticalAssociation,1926(6):149-161.
[151] Box G.E.P., Jenkins G.M., Time Series Analysis, Forecasting and Control. San Francisco:Holden-Day,1976. First published,1970.
[152] Bunn D.W., The Systheses of Predictive Models in Marketing Research [J]. Journal ofMarketing Research,1979,16:280-283.
[153] Bunn D.W., Wright G. Interaction of Judgmental and Statistical Forecasting Methods: Issues&Analysis [J]. Management Science,1991,37:501-518.
[154] Cai Y.Z. A quantile approach to US GNP [J].Economic Modelling,2007,(24):969-979.
[155] Cai Z.W. Regression quantiles for time series [J].Econometric Theory,2002,(1):169-192.
[156] Cao R, Delgado M.A, Gonzalez-manteiga W. Nonparametric curve estimation: an overview[J]. Investigacions Economicals.1997,(2):209-252.
[157] Chaudhuri P. Global nonparametric estimation of conditional quantile functions and theirderivatives [J]. Journal of Multivariate Analysis,1991b.39,246-269.
[158] Chaudhuri P. Nonparametric estimates of regression quantiles and their local Bahadurrepresentation [J]. The Annals of Statistics,1991a.19,760-777.
[159] Chen, M. Y. et al. Relations between health care expenditure and income: an application oflocal quantile regression [J]. Applied Economics Letters,2009,(4):177-181.
[160] CLEMEN R. Combining forecasts: a review and annotated bibliography with discussion [J].International journal of Forecasting,1989(5):559-608.
[161] Clemen R.T., Winkler R.L. Combining Economic Forecasts [J]. Journal of Business andEconomic Statistics,1986,4(1):39-46.
[162] Clements M.P. Quantile forecasts of daily exchange rate returns from forecasts of realizedvolatility [J].Journal of Empirical Finance.2008,(4):729-750.
[163] Clements M.P., Hendry D.F., On the Limitations of Comparing Mean Squared Forecast Errors[J]. Journal of Forecasting,1993,12:617-637.
[164] Clifton B. Cox, Patrick J. Luby.Predicting Hog Prices [J]. Journal of Farm Economics,1956,(11):931-939
[165] Collopy F., Armstrong J.S., Rule-based Forecasting: Development and Validation of anExpert Systems Approach to Combining Time Series Extrapolation [J]. Management Science,1992,38(10):1394-1414.
[166] Cooper J.P., Nelson CR., The ex-ante predication performance of the St Louis andFRB-MIT-PENN econometric model and some results on composite predications [J]. Journal ofMoney, Credit and Banking,1975,7: pp.1-32.
[167] Co kun Hamza ebi. Improving Artificial Neural Networks’ Performance in Seasonal TimeSeries Forecasting [J]. Information Sciences,2008,178(23):4550-4559.
[168] David Enke, Suraphan Thawornwong. The Use of Data Mining and Neural Networks ForForecasting Stock Market Returns [J]. Expert Systems with Applications,2005,39(4):927-940.
[169] David J. Cabedo, Ismael Moya. Estimating oil price ‘Value at Risk’ using the historicalsimulation approach[J]. Energy Economics,2003(25):239–253.
[170] Derek Byerlee. Managing food price risks and instability in a liberalizing marketenvironment:Overview and policy options[J]. Food Policy,2006(31):275–287.
[171] Dickinson J.P. Some Statistical Results in the Combination of Forecasts [J]. OperationalResearch Quarterly (1970-1977),1973,24(2):253-260.
[172] Diebold F.X. Forecast Combination and Encompassing: Reconciling two DivergentLiteratures [J]. International Journal of Forecasting,1989,5:589-592.
[173] DONALDSON R G, KAMSTRA M. Forecast combining with neural networks [J]. Journal ofForecasting,1996,(15):49-61.
[174] Dufrenot G. et al. the trade-growth nexus in the developing countries: a quantile regressionapproach [J].Review of World Economics,2010(4):731-61.
[175] Ehrbeck T., Waldman R., Why are professional forecasters biased? Agency versus behavioralexplanations [J]. The Quaterly Journal of Economics,1996:21-40.
[176] Ericsson N.R., Marquez J., Encompassing the Forecasts of U.S. Trade Balance Models [J].Review of Economics and Statistics,1993,75:19-31.
[177] Ezekiel M. Two methods of forecasting hog prices [J]. Journal Of the American StatisticalAssociation,1927,22(15):22-30
[178] Fang J.Q, Hu T.C, Young T., Robust nonparametric function estimation. Scndinavian Journalof Statistics,1994,21:433-446.
[179] FANG Y., Forecasting combination and encompassing tests [J]. International Journal ofForecasting,2002,(18):421-438.
[180] Farzan Aminian, E.Dante Suarez, Mehran Aminian and Daniel T. Walz. ForecastingEconomic Data with Neural Networks [J]. Computational Economics,2006,28:71-88.
[181] George S. Atsalakis and Kimon P. Valavanis. Forecasting Stock Market Short-term TrendsUsing a Neuro-fuzzy Based Methodology. Expert Systems with Applications,2009,36(7):10696-10707.
[182] Gilles Dufrenot Valerie Mignon. The trade-growth nexus in the developing countries: aquantile regression approach[J]. Rev World Econ,2010,146:731–761.
[183] Giot Pierre. The information content of implied volatility in agricultural commodity market[J].Forthcoming in Journal of Futures Markets,2003,(23):441-451.
[184] Granger C.W.J., Ramanathan R., Improved Methods of Forecasting [J]. Journal forForecasting,1984,(3):197-204
[185] Gupta S., Wilton P.C., Combination of economic forecasts: An odds-matrix approach [J].Journal of Business and Economic Statistics,1987,(6):373-319.
[186] Gustavsen G.W., Rickertsen K. A censored quantile regression analysis of vegetable demand:the effects of changes in prices and total expenditure [J].Canadian Journal of AgriculturalEconomics,2006,(4):631-645.
[187] Hall P. et al. Methods for estimating a conditional distribution function [J].Journal of theAmerican Statistical Association,1999,445:154-163.
[188] Han Dezong. Early warning research of China commodity futures market risk based on VaR[J]. Management Engineering Transaction,2008,22(1)：117~121.
[189] Hao L.X., Naiman D.Q. Quantile regression.Sage Publications Inc, Thousand Oaks, CA.2007.
[190] Harvey D., Leybourne S., Newbold P., Tests for Forecast Encompassing [J]. Journal ofBusiness and Economic Statistics,1998,(16):254-259.
[191] Hendry D.F., Clements M.P., Pooling of Forecast [J]. Econometrics Journal,2002,5:1-26.
[192] Henry L.M. Forecasting The Yield And The Price Of Cotton. New York: The MacmillanCompany,1917:100-113
[193] Hu T et al., A Hybrid Model for Forecasting Aquatic Products Short-Term Price IntegratedWavelet Neural Network with Genetic Algorithm[J]. Springer-Verlag Berlin Heidelberg，2005:352-360.
[194] Ivana Komunjer, Quang Vuong. Efficient estimation in dynamic conditional quantile models[J]. Journal of Econometrics,2010,157:272-285.
[195] Izeman A.J. Recent developments in nonparametric density estimation [J]. Journal of theAmerican statistical Association.1991,86(413):205-224.
[196] Jacobson F.R., Boylan, J.E., Shale E. et al., M. A Robust Forecasting System, Based on theCombination of Two Simple Moving Average [J], Journal of the Operational Research Society,1999,150:1199-1204.
[197] Jacobson T., Karlsson S., Finding Good Predictors for Inflation: A Bayesian Model AverageApproach [J]. Journal of Forecasting,2004.
[198] Jagger T.H., Elsner J.B., Modeling tropical cyclone intensity with quantile regression[J].International Journal of Climatology,2009,(10):1351-1361.
[199] James W. Taylor. Estimating Value at Risk and Expected Shortfall Using Expectiles [J].Journal of Financial Econometrics,2008:231-252.
[200] Jarrett F.G. Short Term Forecasting Of Australian Wool Prices [J]. Australian EconomicPaper,1965,(4):93-102
[201] John M. Marsh. A Rational Distributed Lag Model of Quarterly Live Cattle Prices [J].American Journal of Agricultural Economics,1983,(8):539-547.
[202] Jones M.C, Marron J.S, Sheather S.J. A brief survey of bandwidth selection for densityestimation [J]. Journal of the American Statistical Association,1996,91(433):401-407.
[203] Jr G. B., Koenker R. The asymptotic distribution of the least absolute error estimator [J],Journal of the American Statistical Association,1978,(363):618-622.
[204] Kenneth A. Foster, Arthur M. Havenner, Allan M. Walburger. System Theoretic Time-SeriesForecasts of Weekly Live Cattle Prices [J]. American Journal of Agricultural Economics,1995(11):1012-1023.
[205] KHOTARI S P, WARNER J B. Economics of event studies: Working paper[R]. Center forCorporate Governance, Tuck School of Business at Dartmouth,2005.
[206] Kim S. Harris, Raymond M. Leuthold. A Comparison of Alternative ForecastingTechniques for Livestock Prices: A Case Study [J]. North Central Journal of AgriculturalEconomics,1985(1):40-50.
[207] Koenker R, Hallock K F. Quantile regression [J]. Journal of Economic Perspectives,2008,15:143-56.
[208] Koenker R, Jr G.B. Regression quantiles [J]. Econometrica,1978,46,33-50.
[209] Koenker R., Hallock K.F. Quantile regression [J].Journal of Economic Perspectives,2001,(4):143-156.
[210] Koenker R., Jr G.B. Regression quantiles [J].Econometrica,1978(1):33-50.
[211] Koenker R.W, Dorey V. Computing regression quantiles. Applied Statistics,1987,36:383-93.
[212] Koenker, R. W. and Dorey, V. Computing Regression Quantiles [J]. Applied Statistics,1987,(3):383-393.
[213] Kuan C.M., Hsin-Yi Lin.An encompassing test for non-nested quantile regression models [J].Economics Letters,2010(107):257-260.
[214] Kudryavtsev A.A. Using quantile regression for rate-making [J]. Mathematics andEconomics,2009,(2):296-304.
[215] Kuusisto S., Lehtokangas M., Saarinen J. et al., Short Term Electric Load Forecasting Using aNeural Network with Fuzzy Hidden Neurons [J]. Neural Comput&Applic,1997,6:42-56.
[216] Kym Anderson, Signe Nelgen. Trade Barrier Volatility and Agricultural Price Stabilization.CEPR Discussion Paper. November2010.
[217] LAM K.F, MUI H.W, YUEN H.K., A note on minimizing absolute percentage error incombined forecasts [J]. Computer&Operational Research,2001,(28):1141-1147.
[218] Lawrence M.J., Edmundson R.H., O’Connor M.J., An Examination of the Accurancy ofJudgmental Extrapolation of Time Series [J]. Intermational Journal of Forecasting,1985,1:25-36.
[219] Lawrence M.J., Edmundson R.H., O’Connor M.J., The Accurancy of Combining Judgementaland Statistical Forecasts [J]. Management Science,1986,32(12):1521-1532.
[220] Lawrene N.J., Edmundson R.H., O’Connor M.J., An Examination of the Accurancy ofJudgmental Extrapolation of Time Series [J]. Intermational Journal of Forecasting,1985,(1):25-36.
[221] Lawrene N.J., Edmundson,R.H. and O’Connor, M.J. The Accurancy of CombiningJudgemental and Statistical Forecasts [J]. Management Science,1986,32(12):1521-1532
[222] Leuthold RM et al. Forecasting Daily Hog Prices and Quantities: A Study of AlternativeForecasting Techniques [J]. Journal of the American Statistical Association,1970(5):90-107.
[223] Li Ganqiong, Xu Shiwei, Li Zhemin, Dong Xiaoxia. Using Quantile Regression Approach toAnalyze Price Movements of Agricultural Products [J]. Journal Of Integrative Agriculture,2012,11(4):674-683.
[224] Li Ganqiong, Xu Shiwei, Li Zhemin. Short-Term Price Forecasting For Agro-products UsingArtificial Neural Networks [J]. Agriculture and Agricultural Science Procedia,2010,(1):278-287.
[225] Li M., Y L., Re-examining the risk-return relationship in banks using quantile regression.TheService Industries Journal,2010,30:1871-1881.
[226] Li M.Y.L. Re-examining the risk-return relationship in banks using quantile regression[J].The Service Industries Journal,2010,(11):1871-1881.
[227] Li Q, Racine J. Nonparametric estimation of distributions with categorical and continuousdata [J]. Journal Of Multivariate Analysis,2003,86:266-292.
[228] Li Q., Racine J.S., Nonparametric econometrics, Princeton University Press, New Jersey,USA.2007
[229] Li Y, Singii R.S, Sun YG. Goodness-of-fit tests of a parameric density functions: monte carlosimulation studies [J]. Journal of Statistical Research.2005,39(2):111-133.
[230] Ma L., Pohlman L. Return forecasts and optimal portfolio construction: a quantile regressionapproach[J].The European Journal of Finance,2008,14(5):409-425.
[231] Makridakis S. et al., The Accuracy of Major Extrapolation (Time Series) Methods: Results ofa Forecasting Competition [J]. Journal of Forecasting,1982,1:111-153.
[232] Makridakis S., Hibon M., Accuracy of Forecasting: An Empirical Investigation (WithDiscussion)[J]. Journal of the Royal Statistical Society, Series A,1979,142:97-145.
[233] Makridakis S., Winkler R.L., Average of forecasting: Some Empirical Results [J].Management Science,1983,29(3).
[234] Matthew Butler and Ali Daniyal. Multi-objective Optimization with an Evolutionary ArtificialNeural Network for Financial Forecasting. GECCO’09, July8-12,2009, Montreal Quebec,Canada.
[235] Matthys G. et al. Estimating catastrophic quantile levels for heavy-tailed distributions [J].Mathematics and Economics,2004,34(4):517-537.
[236] MENEZES L.M, BUNN D.W, TAYLOR J.W., Review of guidelines for the use of combinedforecasts [J]. European Journal of Operational Research,2000,(120):190-204.
[237] Mordecai Ezekiel.Two Methods of Forecasting Hog Prices [J]. Journal of the AmericanStatistical Association,1927(5):22-30.
[238] Moriarty M.M., Adams A.J., Management Judgement Forecasts, Composite ForecastingModels and Conditional Efficiency [J]. Journal of Marketing Research,1984,21:239-250.
[239] Morris P.A., Combing Expert Judgments: A Bayesian Approach [J] Management Science,1977,23:679-693.
[240] Nelson C.R., A benchmark for the accuracy of econometric forcastes of GNP [J]. BusinessEconomics,1984,19:52-58.
[241] Nelson C.R., The predication performance of the F.R.B.-M.I.T.-PENN model of the U.S.economy [J]. American Economic Review,1972,62:902-917.
[242] Newbold P., Granger C.W.J., Experience With Forecasting Univariate Time Series andCombination Forecasts [J] Journal of the Royal Statistical Society. Series A (General),1974,137(2).
[243] Nikolay Archak, Panagiotis G., lpeirotis. Modeling Volatility in Prediction Markets. Ec’09,July6-10,2009, Stanford, California, U.S.A.
[244] Nikos Alexandratos. Food Price Surges: Possible Causes, Past Experiences and Relevance forExploring Long-Term Prospects [J]. Population and Development Review,2008.34(4):663–697.
[245] PAI P.F, LIN C.S., A hybrid ARIMA and support vector machines model in stock priceforecasting [J]. The International journal of Management Science,2005(33):497-505.
[246] Paulo Cortez. Evolving Time Series Forecasting ARMA Models [J]. Journal of Heuristics,2004(10):415-429.
[247] Pena D., Combining Information in Statistical Modeling [J], The American Statistician,1977,.51(4):326-332.
[248] Powell J. Least absolute deviation estimation for the censored regression model”[J].Journalof Econometrics,1984,25(3):303-325.
[249] Raúl Pino, José Parreno, Alberto Gomez, et al., Forecasting Next-day Price of Electricity inthe Spanish Energy Market Using Artificial Neural Networks [J]. Engineering Applications ofArtificial Intelligence,2008,21(1):53-62.
[250] Reid D.J., A Comparative Study of Time Series Prediction Techniques on Economic Data,Ph.D. thesis (University of Nottingham),1969.
[251] Richard E.J., Gordon C.R., Commodity Price Forecasting with Large-Scale EconometricModels and the Futures Market American [J]. Journal of Agricultural Economics,1981,(5):197-208.
[252] Sarle C.F. The Forecasting Of The Price Of Hogs[J]. American Economic Review.1925,15(3):1-22
[253] Schmittlein D.C., Kim, J., Morrison D.G., Combining Forecasts: Operational Adjustments toTheoretically Optimal Rules [J]. Management Science,1990,36(9):1044-1056.
[254] Schmitz A, Watts D.G. Forecasting Wheat Yield: An Application Of Parametric Time SeriesModeling [J]. American Journal Of Agricultural Economics.1970,52:247-254
[255] Seok-Oh Jeong, Kee-Hoon Kang. Nonparametric estimation of value-at-risk [J].Journal ofApplied Statistics2009,(12):1225–1238.
[256] Seth Meyer, Wyatt Thompson.Demand Behavior and Commodity Price Volatility UnderEvolving Biofuel Markets and Policies[J]. Natural Resource Management and Policy,2010,(33):133-148.
[257] Shabri A. Nonparametric kernel estimation of annual maximum stream flow quantiles[J].Matematika,2002,18(2):99-107.
[258] Shiliang Sun. Traffic Flow Forecasting Based on Multitask Ensemble Learning. GEC’09,June12-14,2009, Shanghai, China.
[259] Smith B.B. Forecasting The Acreage Of Cotton[J]. Journal Of the American StatisticalAssociation,1925,20(149):31-47
[260] Somers M., Whittaker J., Quantile regression for modeling distributions of profit and loss[J].European Journal of Operational Research,2007,183:1477-1487.
[261] Stock, J.H., Watson M.W., Macroeconomic Forecasting Using Difusion Indexes [J]. Journalof Business and Economic Statistics,2002,20(2):147-162.
[262] Taylor J. W. Forecasting daily supermarket sales using exponentially weighted quantileregression[J].European Journal of Operational Research,2007,178(1):154-167.
[263] Taylor J. W., Bunn D.W., A quantile regression approach to generating prediction intervals[J]. Management Science,1999,(2):225-237.
[264] Taylor J.W, Bunn D.W.,1999. A quantile regression approach to generating predictionintervals [J]. Management Science,145,225-37.
[265] TERUI N, GIJK H.K., Combined forecasts from liner and non-liner time series models [J].International Journal of Forecasting,2002,(18):421-438.
[266] Tezuka Massaru, Munakata Satoshi. Daily Demand Forecasting of New Products UtilizingDiffusion Models and Genetic Algorithms. SAC’09March8-12,2009, Honolulu, Hawai’i, U.S.A.
[267] TIM KREHBIEL, LEE C., ADKINS. PRICE RISK IN THE NYMEX ENERGY COMPLEX:AN EXTREME VALUE APPROACH [J]. Wiley Periodicals, Inc. Jrl Fut Mark,2005,(25):309-337.
[268] Timofeev A. A., Sterin A.M., Using the quantile regression method to analyze changes inclimate characteristics [J].Russian Meteorology and Hydrology,2010,(5):310-319.
[269] Tracy Z., Wu Keming Yu, Yan Yu. Single-index quantile regression [J]. Journal ofMultivariate Analysis,2010,(101):1607-1621.
[270] Trino-Manuel íguez. Volatility and VaR forecasting in the Madrid Stock Exchange. SpanEcon Rev,2008,10:169-296.
[271] Velayoudoum Marimoutou et al. Extreme Value Theory and Value at Risk: Application to oilmarket [J]. Energy Economics,2009,(31):519-530.
[272] Walls W.D., Zhang W., Using Extreme Value Theory to Model Electricity Price Risk With anApplication to the Alberta Power Market [J]. ENERGY EXPLORATION&EXPLOITATION,2006,(23):375-404.
[273] Wang C et al. Measurement of the Fluctuation Risk of the China Fruit Market Price based onVaR[J]. Agriculture and Agricultural Science Procedia,2010,(1):212-218.
[274] WANG S Y, YU L, LAI K K. A Novel hybrid AI system framework for crude oil priceforecasting [C]. CASDMKM2004, LNAI3327,2004:233-242.
[275] WANG S Y, YU L, LAI K K. A rough-set-refined text mining approach for crud oil markettendency forecasting [J]. International journal of knowledge and systems sciences,2005(1):33-46.
[276] WANG S Y, YU L, LAI K K. Crude oil price forecasting with TEL@I Methodology [J].Journal of systems science and complexity,2005(2):145-166.
[277] Wilbur R. Maki.Forecasting Livestock Supplies and Prices with an Econometric Model [J].Journal of Farm Economics,1963,(8):612-624.
[278] Winkle R.L., Makridakis S., The Combination of Forecasts [J] Journal of the Royal StatisticalSociety. Series A (General),1983,146(2).
[279] Winkler R.L., Clemen R.T., Sensitivity of weight in combining Forecasts[J]. OperationResearch,1992,40(3):609-614.
[280] Winkler R.L., Combining Probility Distributions from Dependent Information Sources [J].Management Science,1981,27:276-278.
[281] Yalcinoz T., Eminoglu U., Short Term and Medium Term Power Distribution LoadForecasting By Neural Networks. Energy Conversion and Management,2005,46(10):1393-1405.
[282] Yan Dan, Gong Zhiyong. Measurement of HIS Stock Index Futures Market Risk Based onValue-at-Risk [J].2009International Conference on Information Management.
[283] Yevgeniy B., Sergiy P., Neural Network Approach to Forecasting of Quasiperiodic FinancialTime Series [J]. European Journal of Operational Research,2006,175(3):1357-1366.
[284] Yu K., Jones M.C., Local linear quantile regression [J].Journal of the American StatisticalAssociation,1998,93(441):228-237.
[285] Yue F., Forecasting Combination and Encompassing Tests [J]. Interminational Journal ofForecasting,2003,19:87-94.
[286] ZHANG G.P., Times series forecasting using a hybrid ARIMA and neural network model [J].Neurocomputing,2003,(50):159-175.
[287] Zhang W.S. et al. A Forecast Model of Agricultural and Livestock Products Price [J]. IFIPInternational Federation for Information Processing,2010:40-48.
[288] Zhou Q., Leigh T., Liu C.C., Scenario Generation for Price Forecasting in Restructuredwholesale power markets.2009Power systems Conference&Exposition, March15-18,2009,Seattle, Washington, U.S.A.
[289] Zhou Z., Shi Z.k., Li Y.F., Study to Short-term Flow Estimation at Intersection Base onGenetic Neural Networks. GEC’09, June12-14,2009, Shanghai, China.
[2]曹志广，王安兴，杨军敏.股票收益率非正态性的蒙特卡罗模拟检验[J].财经研究，2005，31（10）：34-41.
[3]陈启欢.中国股票市场收益率分布曲线的实证[J].数理统计与管理，2002，21(5)：9-11.
[4]陈诗一著.非参数支持向量回归和分类理论及其在金融市场预测中的应用[M].北京：北京大学出版社，2008：9-18.
[5]陈思杰，周浩.电力市场电价预测综述[J].继电器，2006（11）：54-60.
[6]陈永福.中国粮食供求预测与对策探讨[J].农业经济问题，2005（4）：8-13.
[7]陈友华，侯定丕.基于预测有效度的优性组合预测模型研究[J].中国科技大学学报，2002，32（2）：172-180.
[8]陈友华.广义加权算术平均组合预测法的最优化理论基础及性质[J].系统工程理论与实践，2003，（4）：37-41.
[9]程国强,徐雪高.改革开放以来我国农产品价格波动的回顾[J].重庆工学院学报:社会科学，2009，23(4)：1-3.
[10]程国强.理性应对粮食价格波动[J].农经观察，2010：8-10.
[11]程国强.粮价异常波动亟须综合调控[J].中国发展观察，2010（6）：5-6.
[12]程瑞芳.我国农产品价格形成机制及波动效应分析[J].中国流通经济，2007，21（3）：22-24.
[13]程贤禄.北京农产品批发市场蔬菜价格预测预报体系研究[J].北京农业科学，2002（2）：1-10.
[14]邓聚龙.灰色系统理论教程[M].武汉：华中理工大学出版社，1990：25-30.
[15]东梅，赵俊杰.市场分形理论与2009年中国小麦价格预测[J].乡镇经济，2009（9）：16-18.
[16]董晓霞，李干琼，刘自杰.农产品市场价格短期预测方法的选择及应用—以鲜奶零售价格为例[J].山东农业科学，2010（1）：109-113.
[17]董晓霞，刘自杰，李志强.我国鲜奶零售价格波动规律及影响因素分析[J].价格月刊，2010,398（7）：6-10.
[18]范振龙.转配股上市对我国股市影响的事件研究[J].复旦学报：自然科学版.2001（2）：220-227.
[19]封建强,王福新.中国股市收益率分布函数研究[J].中国管理科学，2003，11（1）：14-21.
[20]冯文权编著.经济预测与决策技术（第五版）[M].武汉：武汉大学出版社，2008：39-44.
[21]傅如南，林丕源，严尚维，等.基于ARIMA的肉鸡价格预测建模与应用[J].中国畜牧杂志，2008（20）：17-21
[22]高铁梅主编.计量经济分析方法与建模[M].北京：清华大学出版社，2009：295-300.
[23]郭海燕，田新时.极值理论在风险度量中的应用基于上证指数[J].运筹与管理，2004，13（1）：106-111.
[24]郝勇，范军晖编著.系统工程方法与应用[M].北京：科学出版社，2007：62-64.
[25]何启志.国际农产品价格波动风险研究[J].财贸研究，2010，21（5）：63-69.
[26]胡冰川.消费价格指数、农产品价格与货币政策[J].中国农村经济，2010，（12）：37-45.
[27]胡涛.基于非参数的水产品价格预测系统研究[博士论文].北京：中国农业大学经济管理学院.2005.
[28]黄季焜,李宁辉.中国农业政策分析和预测模型——CAPSiM[J].南京农业大学学报（社会科学版），2003，3（2）：30-41.
[29]黄季焜.从农业政策干预程度看中国农产品市场与全球市场的整合[J].世界经济，2008（4）：3-10.
[30]黄季焜.农产品供求视角下农业经济和政策前沿问题研究[J].经济调查，2010（3）：1-7.
[31]黄岩，张国春，王其藩、朱道立.一种新的计算组合预测权重的方法[J].管理工程学报，2001，15（2）：44-46.
[32]黄莹，刘海龙.基于动态VaR模型的我国银行间拆借市场风险度量研究[J].上海管理科学，2008（1）：51-54.
[33]蒋白懿，代进，高金良.城市日用水量预测模型比较研究[J].沈阳建筑大学学报:自然科学版，2008（2）：278-281.
[34]李晖.粮食价格波动与政府调控[J].农村经济，2004（11）：39-40.
[35]李干琼，许世卫，李哲敏，董晓霞.基于非参数核密度估计的中国水果市场收益率分布研究[J].统计与决策，2012（7）：97-100.
[36]李干琼，许世卫，孙益国，李哲敏，董晓霞.中国蔬菜市场价格短期波动与风险评估[J].中国农业科学，2011，44（7）：1502-1511.
[37]李干琼，许世卫，李哲敏，董晓霞.农产品市场价格短期预测方法与模型研究——基于时间序列模型的预测[J].中国农业大学学报，2011，16（2）：172-178.
[38]李干琼，董晓霞，王启现.加拿大种业管理做法[J].世界农业，2010（9）：17-20.
[39]李干琼，许世卫，李哲敏，董晓霞.农产品市场价格超短期预测研究——基于西红柿日批发价格的现代时间序列法建模[J].华中农业大学学报（社科版），2010（6）：40-45.
[40]李干琼，李哲敏.2009年禽蛋市场价格回顾及展望[J].中国食物与营养.2010（2）：50-53.
[41]李国祥.2003年以来我国农产品价格轮番上涨的经济学分析.新视野，2011（1）：19-21.
[42]李国柱，刘德智主编.计量经济学试验教程[M].北京：中国经济出版社，2009：193-220.
[43]李哲敏，李干琼.禽蛋市场价格短期预测[J].中国食物与营养，2010（6）：36-40.
[44]李哲敏，李干琼.禽蛋市场价格及其短期预测因子分析[J].中国食物与营养，2009（12）：36-39.
[45]李哲敏，许世卫，董晓霞，等.中国禽蛋产业链短期市场价格传导机制[J].中国农业科学，2010，43（23）：4951-4962.
[46]李志强.农产品市场分析重点与关键技术[J].农业展望，2010（1）：53-58.
[47]梁来存.我国粮食单产保险纯费率厘定的实证研究[J].统计研究，2010，27（5）：67-73.
[48]廖永松，黄季焜.21世纪我国粮食安全保障与灌溉水需求预测[J].中国水利，2004（1）：36-38.
[49]刘春芳,王济民.我国生猪价格波动原因分析及趋势判断[J].农业展望，2009，5（5）：12-14.
[50]刘春海等.粮食价格预测方法探讨[J].农业发展与金融，2007（6）：84.
[51]刘峰，王儒敬，李传席.ARIMA模型在农产品价格预测中的应用[J].计算机工程与应用，2009，45（25）：238-239.
[52]刘国琴.山东省大葱和大蒜价格波动原因及对策[J].中国蔬菜，2008（10）：4-7.
[53]刘海清,方佳.基于指数平滑模型的海南省芒果价格预测[J].热带农业科学，2010（1）：79-81.
[54]刘洪宇.商品市场价格的三时点预测模型及其应用[J].预测，2001（3）：51-54
[55]刘家富，周慧秋.国内大豆市场价格波动及其影响因素分析[J].东北农业大学学报(社会科学版)，2010，8（4）：10-13.
[56]罗万纯，陈永福.中国大米市场价格共因子分析.中国农村观察，2007（4）：2-12.
[57]吕新业，王济民，吕向东.我国食物安全的短期预测与预警研究[J].农业经济问题，2006，(5)：49-55.
[58]马超群，李红权.风险价值的完全参数方法及其在金融市场风险管理中的应用[J].系统工程理论与实践，2001，21（4）：74-79.
[59]马孝斌，王婷，董霞，等.向量自回归法在生猪价格预测中的应用[J].中国畜牧杂志，2007（23）：4-6
[60]马永开，唐小我，杨桂元.优性组合预测方法存在判别定理[J].预测，1995，（2）：57-58.
[61]马永开，唐小我.多目标组合预测优化模型研究[J].统计研究，1997，（4）：45-48.
[62]马永开，杨桂元，唐小我.非负权重组合预测的冗余定理[J].系统工程理论方法应用，1995，4（4）：33-39.
[63]孟凡新,董彭滔，林小兰.粮食市场价格波动规律、原因及对策分析[J].价格理论与实践，2008（9）：36-37.
[64]宁攸凉.中国生猪价格波动的影响与成因探究[J].畜牧经济，2010，46（2）：52-56.
[65]农业部市场与经济信息司.2009中国农产品市场分析报告[M].北京：中国农业出版社，2010：48-58.
[66]农业部市场与经济信息司.2011中国农产品市场分析报告[M].北京：中国农业出版社，2011：12-50.
[67]农业部市场与经济信息司.农产品市场监测预警工作手册[M].北京：中国农业出本社，2010：100-134.
[68]攀登，邹炎.内幕交易与市场操纵的事件研究[J].当代经济管理，2005（10）：129-136.
[69]平平，刘大有，杨博.组合预测模型在猪肉价格预测中的应用研究[J].计算机工程与科学，2010（5）：109-112.
[70]秦富.河南小麦产业链各环节成本收益研究.农业经济问题，2008（5）：13-19.
[71]沈巍.股票价格预测模型研究[J].财经问题研究，2009（7）：89-93.
[72]沈小燕，杨旭辉.农产品期货价格波动性分析[J].上海第二工业大学学报，2006，23（2）：117-121.
[73]孙见荆.组合预测参数估计方法研究[J].预测，1996（6）：54-56.
[74]孙敬水主编.中级计量经济学[M].上海：上海财经大学出本社，2009：272-318
[75]谭英平.非参数密度估计在个体损失分布中的应用[J].统计研究，2003（8）：40-44.
[76]唐林俊，杨虎.沪深股市收益率分布特征的统计分析[J].数理统计与管理，2004，23（5）：1-4.
[77]唐小我，曹长修，金德运.组合预测最优加权系数向量的进一步研究[J].预测，1994，（2）：48-49.
[78]唐小我，曾勇.组合预测误差校正模型的应用分析[J].管理科学学报，2002，5（6）：53-64.
[79]唐勇，寇贵明.分位数回归视角下的金融市场风险度量研究进展[J].福州大学学报：哲学社会科学版，2009，23（6）：39-44.
[80]滕素珍，刘羽，于建坤.价格预测模型及算法[J].大连理工大学学报，1995(10）：593-595.
[81]田宏伟，詹原瑞.极值理论（EVT）方法用于风险价值（VaR）计算的实证比较与分析[J].系统工程理论与实践，2000（10）：27-35.
[82]汪寿阳，余乐安，房勇，等著.国际油价波动分析与预测[M].湖南：湖南大学出版社，2008：53-64.
[83]汪寿阳.金融危机对全球大宗商品和中国经济的影响[J].中国禽业导刊，2008，25（6）：21-22.
[84]汪同三，王丽.中国计量经济学发展之路.科技促进发展，2010（5）：7-12.
[85]汪同三，张涛等著.组合预测：理论、方法及应用.北京：社会科学文献出版社，2008:58-85.
[86]王川.基于VaR的我国粮食期货市场基差风险度量与分析[J].农村经济与科技，2010（7）：8-11.
[87]王川.我国粮食市场价格的影响因素分析[J].农业经济，2010（7）：24-27.
[88]王吉恒，王新利.农产品市场风险与市场预测研究[J].农业技术经济，2003（3）：1-5.
[89]王丽红，杨汭华，田志宏，等.非参数核密度法厘定玉米区域产量保险费率研究——以河北安国市为例[J].中国农业大学学报，2007，12（1）：90-94.
[90]王明涛.预测方法的有效性分析[J].预测，1994，（6）：57-59.
[91]王强，秦泰增.上证指数的短期预测模型[J].电脑编程技巧与维护，2008（11）：14-15.
[92]王舒鸿.灰色预测模型在鸡蛋价格预测中的应用[J].中国禽业导刊，2008（15）：48-50.
[93]王怡，周应恒，赵文，等.中国苹果市场整合程度及价格波动规律研究[J].南京农业大学学报，2008，31(1)：112-117.
[94]王勇，张浩.小麦期货价格预测的马尔科夫模型[J].安徽农业科学，2008，36（5）：1721
[95]魏振香，李伟娟，李南.影响我国小麦价格变动的因素分析及对策建议[J].分析预测，2010：50-52.
[96]武拉平.农产品地区差价和地区间价格波动规律研究——以小麦、玉米和生猪市场为例[J].农业经济问题，2000（10）：54-58.
[97]肖智，李春红，李潆兵，等.遗传算法在经济混沌组合预测中的应用[J].重庆大学学报，2002，25（11）：143-146.
[98]徐枫.股票价格预测的GARCH模型[J].统计与决策，2006（18）：107-109.
[99]徐国翔.统计预测和决策[M].2版.上海：上海财经大学出版社，2005：261-268
[100]徐雪高.新一轮农产品价格波动周期:特征、机理及影响[J].财经研究，2008，34（8）：110-119.
[101]徐雪高.猪肉价格高位大涨的原因及对宏观经济的影响[J].农业技术经济，2008（3）：4-9.
[102]许世卫，李哲敏，董晓霞，等.中国农产品在产销间价格传导机制研究[J].资源科学，2010，32（11）：2092-2099.
[103]许世卫，李哲敏，李干琼，等.农产品市场价格短期预测研究进展[J].中国农业科学，2011，44（17）：3666-3675.
[104]许世卫.食物安全中的溃堤效应[J].中国食物与营养，2010（8）：8-13.
[105]严广乐，张宁，刘媛华编.系统工程[M].北京：机械工业出版社，2008：227-232
[106]严荣光，严斌剑.对生猪价格波动的探究与思考——基于湖北省2007年以来相关价格的实证分析[J].价格理论与实践，2010（5）：54-55.
[107]杨军，黄季焜，仇焕广，等.国外农产品价格变化对国内价格的影响[J].中国金融，2011，（22）：62-63.
[108]杨奎河.短期电力负荷的智能化预测方法研究[博士论文].陕西：西安电子科技大学计算机学院，2004：7-16.
[109]杨青，曹明，蔡天晔.CVaR-EVT和BMM在极端金融风险管理中的应用研究[J].统计研究，2010，27（6）：78-86.
[110]姚霞，彭汉艮，朱艳，等.时鲜农产品价格预测的ARIMA时序模型构建与运用[J]，农业系统科学与综合研究，2007（1）：89-94.
[111]姚霞，张卫健，朱艳，等.江苏省不同类型蔬菜价格波动的时空特征分析[J].江苏农业科学，2006（5）：190-193.
[112]易丹辉主编.数据分析与Eviews应用[M].北京：中国人民大学出版社，2008：98-103.
[113]喻国平.事件研究：经济和金融研究的重要方法[J].金融与经济.1998（10）：14-15.
[114]曾勇，李玉东，唐小我.简单平均组合预测有效性的应用分析[J].电子科技大学学报，1999，28（1）：84-88.
[115]曾勇，唐小我，曹长修.非负权重最优组合预测方法研究[J].管理工程学报，1995，9（3）：153-161.
[116]曾勇，唐小我，王竹.无偏组合预测模型研究[J].预测，1995（6）：42-45.
[117]曾勇，唐小我.线性规划在非负权重最优组合预测计算中的应用[J].预测，1994（3）：55-56.
[118]张德丰等编著.MATLAB神经网络应用设计[M].北京：机械工业出版社，2009：7-9.
[119]张合成.涉农网络舆情走势与应对[J].新闻战线，2010（4）：66-68.
[120]张化中主编.价格监测及预测预警[M].北京：中国市场出版社，2006：201-218
[121]张慧玲,我国生猪价格周期性波动的规律及其成因分析[J].猪业经济，2010（1）：26-27.
[122]张峭，王川，王克.我国畜产品市场价格风险度量与分析[J].经济问题，2010（3）：90-94.
[123]张峭.中国粮食生产短期预测模型研究[J].农业展望，2006，2（2）：9-12.
[124]张雯丽,李秉龙.我国棉花短期价格波动研究——基于时间序列[J].技术经济，2009，28（4）：88-93.
[125]张小栓.水产品价格预测支持系统研究[博士论文].北京：中国农业大学经济管理学院，2003：1-13.
[126]张兴旺.关于我国农产品市场问题的几点认识与思考[J].农业经济问题，2009（1）：66-69.
[127]张兴旺.我国农业信息分析工作的意义、问题与方向[J].农业图书情报学刊.2008，20（，12）：8-10.
[128]郑小平，高金吉，刘梦婷著.事故预测理论与方法[M].北京：清华大学出版社，2009，129-144.
[129]周传世，刘永清，邹自德.组合预测模型的筛选[J].预测，1995（1）：56-59.
[130]周传世，刘永清.变权重组合预测模型的研究[J].预测，1995（4）：47-57.
[131]周传世，罗国民.加权几何平均组合预测模型及其应用[J].数理统计与管理，1995（2）：17-19
[132]周传世.非线性组合预测及其最优权的确定[J].预测，1994（2）：60-64.
[133]周明磊.事件对国际石油价格影响的时间序列分析[J].数学的认识与实践，2004（8）：12-18.
[134]周宗放，杨春德.组合预测权系数的目标规划确定法[J].系统工程理论方法应用，1995，4（1）：50-55.
[135]朱宏泉，卢祖帝，汪寿阳.Value at risk的核估计理论[J].系统科学与数学，2002，22（3）：365-374.
[136]朱凯，王正林编著.精通MATLAB神经网络[M].北京：电子工业出版社，2010：193-198.
[137]祝金荣著.石油期货价格预测研究[M].北京：冶金工业出本社，2008：83-95.
[138] Abrevaya J., Dahl C.M. The effects of birth inputs on birthweight: evidence from quantileestimation on panel data [J]. Journal of Business&Economic Statistics,2008,(4):379-397.
[139] Al-Qassam M.S., Lane J.A., Forecasting exponential autoregressive models of order1[J].Journal of Time Series Analysis,1989,10:95-113.
[140] Ames W. Taylor. Using Exponentially Weighted Quantile Regression to Estimate Value atRisk and Expected Shortfall [J]. Journal of Financial Econometrics,2008,(6):382-406.
[141] Andrews M.J., Minford P., Peel D., Forecast Encompassing Tests of the Liverpool Model [J].Oxford Bulletin of Economics and Statistics,1995,61:221-256.
[142] Armstrong J.S., et al. Commentary on the Makridakis Time Series Competition(M-competition)[J]. Journal of Forecasting,1983,2:259-311.
[143] Ashton A.H., Ashton R.H. Aggregating Subjective Forecasts: Some Empirical Results [J]Management Science,1985,31(12):1499-1508.
[144] Ashu Jain, Ashish Kumar Varshney, Umesh Chandra Joshi. Short-Term Water DemandForecast Modeling at IIT Kanpur Using Artificial Neural Networks [J]. Water ResourcesManagement,2001,15:299-321.
[145] Banachewicz K., Lucas A., Quantile forecasting for credit risk management using possiblymisspecified hidden markov models [J].Journal of Forecasting,2008,27(7):566-586.
[146] Batchelor R., Dua P., Forecaster Diversity and the Benefits of Combining Forecastes [J]Management Science,1995,41(1):68-75.
[147] Bates J.M, Granger C.W.J., The Combination of Forecasts [J]. Operational ResearchQuarterly,1969,20(4):451-468.
[148] Bollerslev T., Chou R.S., Kroner K.F., ARCH modeling in finance-A review of the theory andempirical evidence. Journal of Econometrics,1992,52:5-59.
[149] Bopp A.E. On Combining Forecasts: Some Extensions and Results [J]. Management Science,1985,31(12).
[150] Bosland C.C. Forecasting the Price of Wheat [J].Journal of the American StatisticalAssociation,1926(6):149-161.
[151] Box G.E.P., Jenkins G.M., Time Series Analysis, Forecasting and Control. San Francisco:Holden-Day,1976. First published,1970.
[152] Bunn D.W., The Systheses of Predictive Models in Marketing Research [J]. Journal ofMarketing Research,1979,16:280-283.
[153] Bunn D.W., Wright G. Interaction of Judgmental and Statistical Forecasting Methods: Issues&Analysis [J]. Management Science,1991,37:501-518.
[154] Cai Y.Z. A quantile approach to US GNP [J].Economic Modelling,2007,(24):969-979.
[155] Cai Z.W. Regression quantiles for time series [J].Econometric Theory,2002,(1):169-192.
[156] Cao R, Delgado M.A, Gonzalez-manteiga W. Nonparametric curve estimation: an overview[J]. Investigacions Economicals.1997,(2):209-252.
[157] Chaudhuri P. Global nonparametric estimation of conditional quantile functions and theirderivatives [J]. Journal of Multivariate Analysis,1991b.39,246-269.
[158] Chaudhuri P. Nonparametric estimates of regression quantiles and their local Bahadurrepresentation [J]. The Annals of Statistics,1991a.19,760-777.
[159] Chen, M. Y. et al. Relations between health care expenditure and income: an application oflocal quantile regression [J]. Applied Economics Letters,2009,(4):177-181.
[160] CLEMEN R. Combining forecasts: a review and annotated bibliography with discussion [J].International journal of Forecasting,1989(5):559-608.
[161] Clemen R.T., Winkler R.L. Combining Economic Forecasts [J]. Journal of Business andEconomic Statistics,1986,4(1):39-46.
[162] Clements M.P. Quantile forecasts of daily exchange rate returns from forecasts of realizedvolatility [J].Journal of Empirical Finance.2008,(4):729-750.
[163] Clements M.P., Hendry D.F., On the Limitations of Comparing Mean Squared Forecast Errors[J]. Journal of Forecasting,1993,12:617-637.
[164] Clifton B. Cox, Patrick J. Luby.Predicting Hog Prices [J]. Journal of Farm Economics,1956,(11):931-939
[165] Collopy F., Armstrong J.S., Rule-based Forecasting: Development and Validation of anExpert Systems Approach to Combining Time Series Extrapolation [J]. Management Science,1992,38(10):1394-1414.
[166] Cooper J.P., Nelson CR., The ex-ante predication performance of the St Louis andFRB-MIT-PENN econometric model and some results on composite predications [J]. Journal ofMoney, Credit and Banking,1975,7: pp.1-32.
[167] Co kun Hamza ebi. Improving Artificial Neural Networks’ Performance in Seasonal TimeSeries Forecasting [J]. Information Sciences,2008,178(23):4550-4559.
[168] David Enke, Suraphan Thawornwong. The Use of Data Mining and Neural Networks ForForecasting Stock Market Returns [J]. Expert Systems with Applications,2005,39(4):927-940.
[169] David J. Cabedo, Ismael Moya. Estimating oil price ‘Value at Risk’ using the historicalsimulation approach[J]. Energy Economics,2003(25):239–253.
[170] Derek Byerlee. Managing food price risks and instability in a liberalizing marketenvironment:Overview and policy options[J]. Food Policy,2006(31):275–287.
[171] Dickinson J.P. Some Statistical Results in the Combination of Forecasts [J]. OperationalResearch Quarterly (1970-1977),1973,24(2):253-260.
[172] Diebold F.X. Forecast Combination and Encompassing: Reconciling two DivergentLiteratures [J]. International Journal of Forecasting,1989,5:589-592.
[173] DONALDSON R G, KAMSTRA M. Forecast combining with neural networks [J]. Journal ofForecasting,1996,(15):49-61.
[174] Dufrenot G. et al. the trade-growth nexus in the developing countries: a quantile regressionapproach [J].Review of World Economics,2010(4):731-61.
[175] Ehrbeck T., Waldman R., Why are professional forecasters biased? Agency versus behavioralexplanations [J]. The Quaterly Journal of Economics,1996:21-40.
[176] Ericsson N.R., Marquez J., Encompassing the Forecasts of U.S. Trade Balance Models [J].Review of Economics and Statistics,1993,75:19-31.
[177] Ezekiel M. Two methods of forecasting hog prices [J]. Journal Of the American StatisticalAssociation,1927,22(15):22-30
[178] Fang J.Q, Hu T.C, Young T., Robust nonparametric function estimation. Scndinavian Journalof Statistics,1994,21:433-446.
[179] FANG Y., Forecasting combination and encompassing tests [J]. International Journal ofForecasting,2002,(18):421-438.
[180] Farzan Aminian, E.Dante Suarez, Mehran Aminian and Daniel T. Walz. ForecastingEconomic Data with Neural Networks [J]. Computational Economics,2006,28:71-88.
[181] George S. Atsalakis and Kimon P. Valavanis. Forecasting Stock Market Short-term TrendsUsing a Neuro-fuzzy Based Methodology. Expert Systems with Applications,2009,36(7):10696-10707.
[182] Gilles Dufrenot Valerie Mignon. The trade-growth nexus in the developing countries: aquantile regression approach[J]. Rev World Econ,2010,146:731–761.
[183] Giot Pierre. The information content of implied volatility in agricultural commodity market[J].Forthcoming in Journal of Futures Markets,2003,(23):441-451.
[184] Granger C.W.J., Ramanathan R., Improved Methods of Forecasting [J]. Journal forForecasting,1984,(3):197-204
[185] Gupta S., Wilton P.C., Combination of economic forecasts: An odds-matrix approach [J].Journal of Business and Economic Statistics,1987,(6):373-319.
[186] Gustavsen G.W., Rickertsen K. A censored quantile regression analysis of vegetable demand:the effects of changes in prices and total expenditure [J].Canadian Journal of AgriculturalEconomics,2006,(4):631-645.
[187] Hall P. et al. Methods for estimating a conditional distribution function [J].Journal of theAmerican Statistical Association,1999,445:154-163.
[188] Han Dezong. Early warning research of China commodity futures market risk based on VaR[J]. Management Engineering Transaction,2008,22(1)：117~121.
[189] Hao L.X., Naiman D.Q. Quantile regression.Sage Publications Inc, Thousand Oaks, CA.2007.
[190] Harvey D., Leybourne S., Newbold P., Tests for Forecast Encompassing [J]. Journal ofBusiness and Economic Statistics,1998,(16):254-259.
[191] Hendry D.F., Clements M.P., Pooling of Forecast [J]. Econometrics Journal,2002,5:1-26.
[192] Henry L.M. Forecasting The Yield And The Price Of Cotton. New York: The MacmillanCompany,1917:100-113
[193] Hu T et al., A Hybrid Model for Forecasting Aquatic Products Short-Term Price IntegratedWavelet Neural Network with Genetic Algorithm[J]. Springer-Verlag Berlin Heidelberg，2005:352-360.
[194] Ivana Komunjer, Quang Vuong. Efficient estimation in dynamic conditional quantile models[J]. Journal of Econometrics,2010,157:272-285.
[195] Izeman A.J. Recent developments in nonparametric density estimation [J]. Journal of theAmerican statistical Association.1991,86(413):205-224.
[196] Jacobson F.R., Boylan, J.E., Shale E. et al., M. A Robust Forecasting System, Based on theCombination of Two Simple Moving Average [J], Journal of the Operational Research Society,1999,150:1199-1204.
[197] Jacobson T., Karlsson S., Finding Good Predictors for Inflation: A Bayesian Model AverageApproach [J]. Journal of Forecasting,2004.
[198] Jagger T.H., Elsner J.B., Modeling tropical cyclone intensity with quantile regression[J].International Journal of Climatology,2009,(10):1351-1361.
[199] James W. Taylor. Estimating Value at Risk and Expected Shortfall Using Expectiles [J].Journal of Financial Econometrics,2008:231-252.
[200] Jarrett F.G. Short Term Forecasting Of Australian Wool Prices [J]. Australian EconomicPaper,1965,(4):93-102
[201] John M. Marsh. A Rational Distributed Lag Model of Quarterly Live Cattle Prices [J].American Journal of Agricultural Economics,1983,(8):539-547.
[202] Jones M.C, Marron J.S, Sheather S.J. A brief survey of bandwidth selection for densityestimation [J]. Journal of the American Statistical Association,1996,91(433):401-407.
[203] Jr G. B., Koenker R. The asymptotic distribution of the least absolute error estimator [J],Journal of the American Statistical Association,1978,(363):618-622.
[204] Kenneth A. Foster, Arthur M. Havenner, Allan M. Walburger. System Theoretic Time-SeriesForecasts of Weekly Live Cattle Prices [J]. American Journal of Agricultural Economics,1995(11):1012-1023.
[205] KHOTARI S P, WARNER J B. Economics of event studies: Working paper[R]. Center forCorporate Governance, Tuck School of Business at Dartmouth,2005.
[206] Kim S. Harris, Raymond M. Leuthold. A Comparison of Alternative ForecastingTechniques for Livestock Prices: A Case Study [J]. North Central Journal of AgriculturalEconomics,1985(1):40-50.
[207] Koenker R, Hallock K F. Quantile regression [J]. Journal of Economic Perspectives,2008,15:143-56.
[208] Koenker R, Jr G.B. Regression quantiles [J]. Econometrica,1978,46,33-50.
[209] Koenker R., Hallock K.F. Quantile regression [J].Journal of Economic Perspectives,2001,(4):143-156.
[210] Koenker R., Jr G.B. Regression quantiles [J].Econometrica,1978(1):33-50.
[211] Koenker R.W, Dorey V. Computing regression quantiles. Applied Statistics,1987,36:383-93.
[212] Koenker, R. W. and Dorey, V. Computing Regression Quantiles [J]. Applied Statistics,1987,(3):383-393.
[213] Kuan C.M., Hsin-Yi Lin.An encompassing test for non-nested quantile regression models [J].Economics Letters,2010(107):257-260.
[214] Kudryavtsev A.A. Using quantile regression for rate-making [J]. Mathematics andEconomics,2009,(2):296-304.
[215] Kuusisto S., Lehtokangas M., Saarinen J. et al., Short Term Electric Load Forecasting Using aNeural Network with Fuzzy Hidden Neurons [J]. Neural Comput&Applic,1997,6:42-56.
[216] Kym Anderson, Signe Nelgen. Trade Barrier Volatility and Agricultural Price Stabilization.CEPR Discussion Paper. November2010.
[217] LAM K.F, MUI H.W, YUEN H.K., A note on minimizing absolute percentage error incombined forecasts [J]. Computer&Operational Research,2001,(28):1141-1147.
[218] Lawrence M.J., Edmundson R.H., O’Connor M.J., An Examination of the Accurancy ofJudgmental Extrapolation of Time Series [J]. Intermational Journal of Forecasting,1985,1:25-36.
[219] Lawrence M.J., Edmundson R.H., O’Connor M.J., The Accurancy of Combining Judgementaland Statistical Forecasts [J]. Management Science,1986,32(12):1521-1532.
[220] Lawrene N.J., Edmundson R.H., O’Connor M.J., An Examination of the Accurancy ofJudgmental Extrapolation of Time Series [J]. Intermational Journal of Forecasting,1985,(1):25-36.
[221] Lawrene N.J., Edmundson,R.H. and O’Connor, M.J. The Accurancy of CombiningJudgemental and Statistical Forecasts [J]. Management Science,1986,32(12):1521-1532
[222] Leuthold RM et al. Forecasting Daily Hog Prices and Quantities: A Study of AlternativeForecasting Techniques [J]. Journal of the American Statistical Association,1970(5):90-107.
[223] Li Ganqiong, Xu Shiwei, Li Zhemin, Dong Xiaoxia. Using Quantile Regression Approach toAnalyze Price Movements of Agricultural Products [J]. Journal Of Integrative Agriculture,2012,11(4):674-683.
[224] Li Ganqiong, Xu Shiwei, Li Zhemin. Short-Term Price Forecasting For Agro-products UsingArtificial Neural Networks [J]. Agriculture and Agricultural Science Procedia,2010,(1):278-287.
[225] Li M., Y L., Re-examining the risk-return relationship in banks using quantile regression.TheService Industries Journal,2010,30:1871-1881.
[226] Li M.Y.L. Re-examining the risk-return relationship in banks using quantile regression[J].The Service Industries Journal,2010,(11):1871-1881.
[227] Li Q, Racine J. Nonparametric estimation of distributions with categorical and continuousdata [J]. Journal Of Multivariate Analysis,2003,86:266-292.
[228] Li Q., Racine J.S., Nonparametric econometrics, Princeton University Press, New Jersey,USA.2007
[229] Li Y, Singii R.S, Sun YG. Goodness-of-fit tests of a parameric density functions: monte carlosimulation studies [J]. Journal of Statistical Research.2005,39(2):111-133.
[230] Ma L., Pohlman L. Return forecasts and optimal portfolio construction: a quantile regressionapproach[J].The European Journal of Finance,2008,14(5):409-425.
[231] Makridakis S. et al., The Accuracy of Major Extrapolation (Time Series) Methods: Results ofa Forecasting Competition [J]. Journal of Forecasting,1982,1:111-153.
[232] Makridakis S., Hibon M., Accuracy of Forecasting: An Empirical Investigation (WithDiscussion)[J]. Journal of the Royal Statistical Society, Series A,1979,142:97-145.
[233] Makridakis S., Winkler R.L., Average of forecasting: Some Empirical Results [J].Management Science,1983,29(3).
[234] Matthew Butler and Ali Daniyal. Multi-objective Optimization with an Evolutionary ArtificialNeural Network for Financial Forecasting. GECCO’09, July8-12,2009, Montreal Quebec,Canada.
[235] Matthys G. et al. Estimating catastrophic quantile levels for heavy-tailed distributions [J].Mathematics and Economics,2004,34(4):517-537.
[236] MENEZES L.M, BUNN D.W, TAYLOR J.W., Review of guidelines for the use of combinedforecasts [J]. European Journal of Operational Research,2000,(120):190-204.
[237] Mordecai Ezekiel.Two Methods of Forecasting Hog Prices [J]. Journal of the AmericanStatistical Association,1927(5):22-30.
[238] Moriarty M.M., Adams A.J., Management Judgement Forecasts, Composite ForecastingModels and Conditional Efficiency [J]. Journal of Marketing Research,1984,21:239-250.
[239] Morris P.A., Combing Expert Judgments: A Bayesian Approach [J] Management Science,1977,23:679-693.
[240] Nelson C.R., A benchmark for the accuracy of econometric forcastes of GNP [J]. BusinessEconomics,1984,19:52-58.
[241] Nelson C.R., The predication performance of the F.R.B.-M.I.T.-PENN model of the U.S.economy [J]. American Economic Review,1972,62:902-917.
[242] Newbold P., Granger C.W.J., Experience With Forecasting Univariate Time Series andCombination Forecasts [J] Journal of the Royal Statistical Society. Series A (General),1974,137(2).
[243] Nikolay Archak, Panagiotis G., lpeirotis. Modeling Volatility in Prediction Markets. Ec’09,July6-10,2009, Stanford, California, U.S.A.
[244] Nikos Alexandratos. Food Price Surges: Possible Causes, Past Experiences and Relevance forExploring Long-Term Prospects [J]. Population and Development Review,2008.34(4):663–697.
[245] PAI P.F, LIN C.S., A hybrid ARIMA and support vector machines model in stock priceforecasting [J]. The International journal of Management Science,2005(33):497-505.
[246] Paulo Cortez. Evolving Time Series Forecasting ARMA Models [J]. Journal of Heuristics,2004(10):415-429.
[247] Pena D., Combining Information in Statistical Modeling [J], The American Statistician,1977,.51(4):326-332.
[248] Powell J. Least absolute deviation estimation for the censored regression model”[J].Journalof Econometrics,1984,25(3):303-325.
[249] Raúl Pino, José Parreno, Alberto Gomez, et al., Forecasting Next-day Price of Electricity inthe Spanish Energy Market Using Artificial Neural Networks [J]. Engineering Applications ofArtificial Intelligence,2008,21(1):53-62.
[250] Reid D.J., A Comparative Study of Time Series Prediction Techniques on Economic Data,Ph.D. thesis (University of Nottingham),1969.
[251] Richard E.J., Gordon C.R., Commodity Price Forecasting with Large-Scale EconometricModels and the Futures Market American [J]. Journal of Agricultural Economics,1981,(5):197-208.
[252] Sarle C.F. The Forecasting Of The Price Of Hogs[J]. American Economic Review.1925,15(3):1-22
[253] Schmittlein D.C., Kim, J., Morrison D.G., Combining Forecasts: Operational Adjustments toTheoretically Optimal Rules [J]. Management Science,1990,36(9):1044-1056.
[254] Schmitz A, Watts D.G. Forecasting Wheat Yield: An Application Of Parametric Time SeriesModeling [J]. American Journal Of Agricultural Economics.1970,52:247-254
[255] Seok-Oh Jeong, Kee-Hoon Kang. Nonparametric estimation of value-at-risk [J].Journal ofApplied Statistics2009,(12):1225–1238.
[256] Seth Meyer, Wyatt Thompson.Demand Behavior and Commodity Price Volatility UnderEvolving Biofuel Markets and Policies[J]. Natural Resource Management and Policy,2010,(33):133-148.
[257] Shabri A. Nonparametric kernel estimation of annual maximum stream flow quantiles[J].Matematika,2002,18(2):99-107.
[258] Shiliang Sun. Traffic Flow Forecasting Based on Multitask Ensemble Learning. GEC’09,June12-14,2009, Shanghai, China.
[259] Smith B.B. Forecasting The Acreage Of Cotton[J]. Journal Of the American StatisticalAssociation,1925,20(149):31-47
[260] Somers M., Whittaker J., Quantile regression for modeling distributions of profit and loss[J].European Journal of Operational Research,2007,183:1477-1487.
[261] Stock, J.H., Watson M.W., Macroeconomic Forecasting Using Difusion Indexes [J]. Journalof Business and Economic Statistics,2002,20(2):147-162.
[262] Taylor J. W. Forecasting daily supermarket sales using exponentially weighted quantileregression[J].European Journal of Operational Research,2007,178(1):154-167.
[263] Taylor J. W., Bunn D.W., A quantile regression approach to generating prediction intervals[J]. Management Science,1999,(2):225-237.
[264] Taylor J.W, Bunn D.W.,1999. A quantile regression approach to generating predictionintervals [J]. Management Science,145,225-37.
[265] TERUI N, GIJK H.K., Combined forecasts from liner and non-liner time series models [J].International Journal of Forecasting,2002,(18):421-438.
[266] Tezuka Massaru, Munakata Satoshi. Daily Demand Forecasting of New Products UtilizingDiffusion Models and Genetic Algorithms. SAC’09March8-12,2009, Honolulu, Hawai’i, U.S.A.
[267] TIM KREHBIEL, LEE C., ADKINS. PRICE RISK IN THE NYMEX ENERGY COMPLEX:AN EXTREME VALUE APPROACH [J]. Wiley Periodicals, Inc. Jrl Fut Mark,2005,(25):309-337.
[268] Timofeev A. A., Sterin A.M., Using the quantile regression method to analyze changes inclimate characteristics [J].Russian Meteorology and Hydrology,2010,(5):310-319.
[269] Tracy Z., Wu Keming Yu, Yan Yu. Single-index quantile regression [J]. Journal ofMultivariate Analysis,2010,(101):1607-1621.
[270] Trino-Manuel íguez. Volatility and VaR forecasting in the Madrid Stock Exchange. SpanEcon Rev,2008,10:169-296.
[271] Velayoudoum Marimoutou et al. Extreme Value Theory and Value at Risk: Application to oilmarket [J]. Energy Economics,2009,(31):519-530.
[272] Walls W.D., Zhang W., Using Extreme Value Theory to Model Electricity Price Risk With anApplication to the Alberta Power Market [J]. ENERGY EXPLORATION&EXPLOITATION,2006,(23):375-404.
[273] Wang C et al. Measurement of the Fluctuation Risk of the China Fruit Market Price based onVaR[J]. Agriculture and Agricultural Science Procedia,2010,(1):212-218.
[274] WANG S Y, YU L, LAI K K. A Novel hybrid AI system framework for crude oil priceforecasting [C]. CASDMKM2004, LNAI3327,2004:233-242.
[275] WANG S Y, YU L, LAI K K. A rough-set-refined text mining approach for crud oil markettendency forecasting [J]. International journal of knowledge and systems sciences,2005(1):33-46.
[276] WANG S Y, YU L, LAI K K. Crude oil price forecasting with TEL@I Methodology [J].Journal of systems science and complexity,2005(2):145-166.
[277] Wilbur R. Maki.Forecasting Livestock Supplies and Prices with an Econometric Model [J].Journal of Farm Economics,1963,(8):612-624.
[278] Winkle R.L., Makridakis S., The Combination of Forecasts [J] Journal of the Royal StatisticalSociety. Series A (General),1983,146(2).
[279] Winkler R.L., Clemen R.T., Sensitivity of weight in combining Forecasts[J]. OperationResearch,1992,40(3):609-614.
[280] Winkler R.L., Combining Probility Distributions from Dependent Information Sources [J].Management Science,1981,27:276-278.
[281] Yalcinoz T., Eminoglu U., Short Term and Medium Term Power Distribution LoadForecasting By Neural Networks. Energy Conversion and Management,2005,46(10):1393-1405.
[282] Yan Dan, Gong Zhiyong. Measurement of HIS Stock Index Futures Market Risk Based onValue-at-Risk [J].2009International Conference on Information Management.
[283] Yevgeniy B., Sergiy P., Neural Network Approach to Forecasting of Quasiperiodic FinancialTime Series [J]. European Journal of Operational Research,2006,175(3):1357-1366.
[284] Yu K., Jones M.C., Local linear quantile regression [J].Journal of the American StatisticalAssociation,1998,93(441):228-237.
[285] Yue F., Forecasting Combination and Encompassing Tests [J]. Interminational Journal ofForecasting,2003,19:87-94.
[286] ZHANG G.P., Times series forecasting using a hybrid ARIMA and neural network model [J].Neurocomputing,2003,(50):159-175.
[287] Zhang W.S. et al. A Forecast Model of Agricultural and Livestock Products Price [J]. IFIPInternational Federation for Information Processing,2010:40-48.
[288] Zhou Q., Leigh T., Liu C.C., Scenario Generation for Price Forecasting in Restructuredwholesale power markets.2009Power systems Conference&Exposition, March15-18,2009,Seattle, Washington, U.S.A.
[289] Zhou Z., Shi Z.k., Li Y.F., Study to Short-term Flow Estimation at Intersection Base onGenetic Neural Networks. GEC’09, June12-14,2009, Shanghai, China.