Application of Grey Model and Neural Network in Financial Revenue Forecast

There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting.

Fiscal Transfer, Education Investment and Long-Term Educational Performance

Reasonable allocation of educational powers and expenditure responsibilities between central and local government is crucial to the development of education.The reason lies in the fact that local governments have relatively insufficient incentives to invest in education by using local fiscal revenues,while the central government,which pursues the maximization of the interests of the whole society,could promote education and other public services with spatial spilloves.The fiscal transfer payment has made up for the shortage of local investment in education.This paper uses 2010 census(micro data)and macro fiscal data to verify the effects above.Based on the year of birth and place,this paper constructs the proportion of fiscal transfers for compulsory education in the total fiscal revenue(local fiscal revenue and fiscal transfers)to reflect its structural effect.It is found that every 10%increase in the proportion of fiscal transfers brings at least additional 0.2 year of schoolings for local residents,and the effect of special transfer payments accounts for a larger share,among the three types of transfer payment.In the mechanism test,we find that transfer payment can effectively increase local education expenditure and produce an obvious structural effect.Based on this,in order to further improve the long-term educational performance of individuals,we believe that it is necessary to improve the incentive effect of the transfer payment system on common power and the division of expenditure responsibilities in the field of education.