Research on the Forecast and Development of China’s Public Fiscal Revenue Based on ARIMA Model

Qingjie Liu^*, Huachun Wang

School of Government, Beijing Normal University, Beijing, China.

DOI: 10.4236/tel.[phone]                

Abstract

To promote the preparation of the financial budget of more scientific and reasonable, this study adopts the revenue and expenditure data from 1950 to 2013, and applying Johansen cointegration test, error correction model and Granger causality test of causation financial statements between income and expenditure. The result is that there exists long-run equilibrium relationship between spending and revenue caused by this principle by using the moving average difference sequence auto regression model and the least-squares regression fitting equation. The choice of revenue analysis and forecasting finds the optimal model and provides more accurate prediction effects for the budget constraints of the prospective shift from preparation to establish multi-year balanced budget to provide reference, and future revenue growth slowed projections indicate an active role in the conclusion of the people's livelihood-oriented public finances construction being taking place.

Keywords

Public Finance Revenue, Finance Budget, ARIMA Model

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Baghestani, H. and McNown, R. (1994) Do Revenues or Expenditures Respond to Budgetary Disequilibria? Southern Economic Journal, 61, 311-322. http://dx.doi.org/10.2307/1059979
[2]	Chang, T.Y. and Ho, Y.H. (2002) A Note on Testing “Tax-and-Spend, Spend-and-Tax or Fiscal Synchronization”: The Case of China. Journal of Economic Development, 27, 151-160.
[3]	Li, X.M. (2001) Government Revenue, Government Expenditure, and Temporal Causality: Evidence from China. Applied Economics, 33, 485-497. http://dx.doi.org/10.1080/00036840122982
[4]	Wu, K. and Min, C.W. (2006) Empirical Study of China’s Fiscal Revenue System of Separation Problems. Statistical Research, 6, 22-27.
[5]	Ma, Z.H. (2008) Chinese Local Fiscal Revenue and Expenditure—Panel Data Causality and Cointegration Study. Management World, 3, 40-57.
[6]	Roy Meyers, T. (2005) Classic Public Budget—For the Performance of a New Development. Gou Yan Nan Jing Dong Translation, Shanghai University of Finance and Economics Press, Shanghai, 263-264.
[7]	Lin, G.X. and Xuan, H.Y. (2005) GA and BP Artificial Neural Networks in Tax Prediction. Systems Engineering Theory Methods, 2, 145-148.
[8]	Liu, L., Tang, H.P. and Zhang, L.J. (2010) Analysis of Financial Time Series ARMA Model Based on Investment in Education. Beijing Normal University, Beijing, 194-196.
[9]	Gao, C.S., Li, J.F. and Xu, Z.Z. (2011) Expenditure Study (1978-2009) Based on ARMA Model of Administrative Costs. East China Economic Management, 1, 51-53.
[10]	Hou, W.L. (2011) Research on the Stock Index Volatility Stress Test Scenarios Design Based ARMA-GARCH Model. Contemporary Finance & Economics, 11, 71-79.
[11]	Zhang, Q., Wang, Y. and Li, X. (2012) Prospective Credit Quality Forecasting and Stress Testing—Based on ARMA Model and the VAR Model. Financial Forum, 5, 19-25.
[12]	Ou, T.H. (2007) Prediction of Real Estate Price Index Based on ARMA Model. Statistics and Decision, 14, 92-93.