标题:
广州市财政收入分析及预测模型Analysis and Prediction Model of Financial Income in Guangzhou
作者:
陈庚, 卢丹丹, 万浩文, 王国长
关键字:
财政收入, Adaptive-Lasso, 灰色预测, BP神经网络Financial Income, Adaptive-Lasso, Grey Prediction, BP Neural Network
期刊名称:
《Statistics and Application》, Vol.4 No.3, 2015-09-18
摘要:
地方财政收入是国家财政收入的重要组成部分。利用1994~2013年广州市的财政收入相关数据，建立Adaptive-Lasso变量选择模型，并自动识别出广州市财政收入的关键影响因素。并且在变量选择的基础上，又构建了灰色预测与BP神经网络的组合模型来预测广州市2014、2015年的财政收入。模型分析结果表明，社会从业人数、在岗职工工资总额、社会消费品零售总额、城镇居民人均可支配收入、城镇居民人均消费性支出以及全社会固定资产投资额与财政收入的关联性较大，之后进行的组合模型预测也有较好的效果。最后，根据分析结果提出相关的政策性建议。
Local financial revenue is an important part of national fiscal revenues. In order to identify the impact affecting factors of Guangzhou’s fiscal revenue automatically, we established a variable se-lection model in Adaptive-Lasso based on 1994-2013 years’ economic data. Under the research above, the paper offered the predictive value of fiscal revenue from 2014 to 2015 based on grey prediction and BP neural network combined model. The results of the variable selection models showed that the social number of employees, the total of worker’s salary, total volume of retail sales of the social consumer goods, per capita disposable income in urban residents, per capita expenditure on consumption in urban residents and social fixed assets investment were more re-lated to fiscal revenue; afterwards the combined model had better effects. Furthermore, some ad-vices were presented.