基于Lasso和BP神经网络的组合预测及其应用——以居民消费支出预测为例

在变量选择的基础上，构建基于 Lasso 方法和 BP 神经网络的预测模型，并对我国城乡居民的消费支出进行预测，结果显示：基于 Lasso 方法和 BP 神经网络的组合预测精度要明显高于 BP 神经网络、Lasso方法的预测精度；在2014~2020年，我国农村居民消费增长率有所提升，城镇居民消费增长率减缓，城乡居民消费增长率之间的差距呈下降趋势，但短期内城乡居民消费差距依然难以缓和。

The Study on Prediction of Residents Consumption Expenditure based on Lasso and BP Neural Network

On the basis of variable selection,created a multivariate prediction model based on the combination of Lasso method and BP neural network ,and prediction of China's urban and ru-ral residents consumption expenditure.The prediction results showed that:the combination of Lasso method and BP neural network prediction accuracy is higher than that of the BP neural net-work,the Lasso method,the results also showed that in 2014-2020 years,the growth rate of ru-ral residents consumption has improved,the consumption of urban residents increased slowly,the gap between urban and rural consumption rate showed a downward trend,but the gap between ur-ban and rural consumption is still difficult to ease in the short term.