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Spatial approach in forecasting tax revenues

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  • M. Mokliak, P. Chernov, A. Vdovychenko, A. Zubritskyi

Abstract

Study of tax revenue forecasting accuracy is an integral part of the planning and analysis of the tax system indicators as well as one of the central functions of the State Fiscal Service of Ukraine (SFS). This process is complicated by the lack of development of complex statistical apparatus that can be applied for forecasting purposes taking into account the size of tax data available in SFS of Ukraine. The purpose of this article is to demonstrate the advantages of using panel regressions of various fit in forecasting tax revenues at the regional level. To analyze the advantages and disadvantages of usage panel data structure in forecasting tax revenues by region, the authors employ econometric tools, including univariate time series models for each region, spatial and dynamic panel regression. The scientific result is that, on the basis of the analysis, the authors prove the feasibility of using panel regressions with the purpose of forecasting taxes in cases with limited time series data. These developments in modeling and forecasting tax revenues also have practical value, because they can be used directly in the analytical activities of SFS of Ukraine at the regional level.

Suggested Citation

  • M. Mokliak, P. Chernov, A. Vdovychenko, A. Zubritskyi, 2015. "Spatial approach in forecasting tax revenues," Economy and Forecasting, Valeriy Heyets, issue 2, pages 7-20.
    • Handle: RePEc:eip:journl:y:2015:i:2:p:7-20
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    File URL: http://eip.org.ua/docs/EP_15_2_07_uk.pdf
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    1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    2. Robert B. Litterman & Thomas M. Supel, 1983. "Using vector autoregressions to measure the uncertainty in Minnesota's revenue forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 7(Spr).
    3. David M. Drukker & Hua Peng & Ingmar Prucha & Rafal Raciborski, 2013. "Creating and managing spatial-weighting matrices with the spmat command," Stata Journal, StataCorp LP, vol. 13(2), pages 242-286, June.
    4. Mocan, H. Naci & Azad, Sam, 1995. "Accuracy and rationality of state General Fund Revenue forecasts: Evidence from panel data," International Journal of Forecasting, Elsevier, vol. 11(3), pages 417-427, September.
    5. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    6. David M. Drukker & Ingmar Prucha & Rafal Raciborski, 2013. "A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables," Stata Journal, StataCorp LP, vol. 13(2), pages 287-301, June.
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