似乎不相关模型在粮食产量研究中的应用

Abstract

对粮食产量的研究一般采用传统的线性模型，假设方程误差项相互独立，采用最小二乘法进行参数估计。为提高模型的估计精度，引入似乎不相关模型，以1990~2009年稻谷、小麦、玉米的产量、种植面积和有效灌溉面积数据分别建立最小二乘模型和似乎不相关模型。结果显示似乎不相关模型的参数方差一致小于最小二乘模型，用两种模型分别对2010~2015年的三种粮食产量进行预测，似乎不相关模型预测的平均误差更小。说明三种粮食的产量之间存在一定的相关性，似乎不相关模型较传统的最小二乘模型效果更好。 The traditional linear model is usually used in the study of grain yield ,in which the error terms are assumed independent of each other, and the parameters are estimated by the least squares method. In order to improve the estimation accuracy, seemingly unrelated model is introduced. Least squares model and seemingly unrelated model were built with the data that the yield, planting area and irrigation area of rice, wheat and corn between 1990 and 2009. The result showed that parameter variance of seemingly unrelated model is smaller than the least square model. The output of the three kinds of grain between 2010 and 2015 was predicted by the two models, and the average prediction error of seemingly unrelated model was smaller. It shows that there is some correlation between the yields of the three grain crops, and the seemingly unrelated model is better than the traditional least square model.