一种可直接用于预测的神经网络消费函数模型

Artificial Neural Network Model Directly Applicable in Consumption Forecast

目前几乎所有的消费函数模型在实际应用时都有一个共同的不足之处,即在对下一期的消费额进行预测时,模型需要使用的相关数据都直接或间接与下一期的统计数据有关,所以在使用模型前首先要对未知的相关数据进行预测,这样势必会造成误差的积累。本文在对传统的消费函数模型进行分析的基础上,建立了一种可直接用于预测的神经网络消费函数模型,这种模型只需使用前期的相关数据即可进行预测,且预测精度高于传统的消费函数模型。

At present,almost all consumption models have the same shortcoming in practical use.The model parameter has a direct or indirect relation with the statistic result of next period when one wants to forecast the consumption of next period.Therefore,before one uses these models,the interrelated parameter needed must be forecasted before fitting them into the model.However,this will cause accumulated errors.In order to solve this problem,a model that can be directly used in consumption forecast based on the traditional consumption model theory is advanced.