Journal of Economics and Financial Analysis, 4 (2), pp. 63-99, [2020]
URI: https://ojs.tripaledu.com/index.php/jefa/article/view/58/70

Forecasting Value Added Tax Revenue in Ghana





DOI: http://dx.doi.org/10.1991/jefa.v4i2.a37

Abstract

Governments need accurate tax revenue forecast figures for good economic planning but there seems to be no consensus on which method is the most suitable to deliver reliable results leading to differences in the choice of technique from one country to another. This study therefore forecasts Ghana’s Value Added Tax (VAT) Revenue by comparing two methods, ARIMA with Intervention and Holt linear trend methods to establish the one with more precise predictive powers for VAT Revenue. Monthly VAT revenue data from the year 2002 to 2019 is used in the analysis. The findings show that ARIMA with Intervention method outperformed the Holt linear trend model in terms of accuracy and precision. A comparison of predicted results from the ARIMA with intervention model from 2017 to 2019 with Ghana Revenue Authority’s VAT revenue targets based on their in-house forecasting model for the same period reveals that the ARIMA with intervention approach performs better than the in-house forecasting model of the VAT authority. In this case, the study recommends the ARIMA with intervention method to the tax authority for consideration in its forecasting.

Keywords

Value Added Tax (VAT); Forecasting; ARIMA; Holt linear trend; Fiscal Policy Ghana.

JEL Classification

C53, H20.

Full Text:


References

Antwi, S., Ebenezer, A.M.F.E., & Zhao, X. (2012). Impact of VAT rate changes on VAT revenue in Ghana. International Journal of Social Science Tomorrow, 10(1), pp. 1-10

Baguestani, H., & McNown, R. (1992). Forecasting the federal budget with time‐series models. Journal of Forecasting, 11(2), pp. 127-139.

Bird, R.M., Gendron, P.P., (2007). The VAT in Developing and Transitional Countries. Cambridge University Press, Cambridge.

Bird, R.M., Martinez-Vazquez, J., & Torgler, B. (2008). Tax effort in developing countries and high-income countries: The impact of corruption, voice, and accountability. Economic Analysis and Policy, 38(1), pp. 55-71.

Botric, V., & Vizek, M. (2012). Forecasting fiscal revenues in a transition country: the case of Croatia. Zagreb International Review of Economics & Business, 15(1), pp. 23-36.

Box, G.E., & Tiao, G.C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70(349), pp. 70-79.

Box, G.E., Jenkins, G.M., & Reinsel, G.C. (2008). Time Series Analysis. John Wiley & Sons, New Jersey.

Charlet, A., and Jeffrey, O. (2010). An International Perspective on VAT. Tax Notes International, 59(12), pp. 943-954.

Darkwah, K.F., Okyere, G.A., & Boakye, A. (2012).Intervention analysis of serious crimes in the eastern region of Ghana. International Journal of Business and Social Research, 2(7), pp. 132-138

De Mello, L. (2009) Avoiding the Value Added Tax: Theory and cross-country evidence. Public Finance Review, 6(2), pp. 27-46.

Ebrill, L., Keen, M., Bodin, J.P., & Summers, V. (2001). The Modern VAT. Washington, DC. International Monetary Fund.

Edzie-Dadzie, J. (2013). Time Series Analysis of Value Added Tax Revenue Collection in Ghana. Department of Mathematics, Kwame Nkrumah University of Science and Technology.

Favero, C.A., & Marcellino, M. (2005).Modelling and forecasting fiscal variables for the Euro Area. Oxford Bulletin of Economics and Statistics, 67, pp. 755-783.

Fomby, T.B. (2008). Exponential Smoothing Models. Department of Economics, Southern Methodist University Dallas.

Fullerton, T.M. (1989). A composite approach to forecasting state government revenues: A case study of the Idaho sales tax. International Journal of Forecasting, 5(4), pp. 373-80.

Gardner, E.S., & McKenzie, E.D. (1985).Forecasting trends in time series. Management Science, 31(10), pp. 1237-1246.

Gardner, E.S. (1998). Evaluating forecast performance in an inventory control system. Management Science, 36(4), pp. 490-499.

Gebauer, A., Nam, C.W., & Parsche, R. (2007). Can reform models of value-added taxation stop the VAT evasion and revenue shortfalls in the EU? Journal of Economic Policy Reform, 10(1), pp. 1-13.

Heady, C. (2002). Tax policy in developing countries: what can be learned from OECD experience? In the seminar "Taxing Perspectives: A Democratic Approach to Public Finance in Developing Countries" Institute of Development Studies, University of Sussex, UK, October (pp. 28-29).

Hyndman, R.J., Koehler, A.B., Snyder, R.D., & Grose, S. (2002). A state-space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), pp. 439-454.

Igbinosa, E.P. (2016). Empirical Analysis of the Effects of Taxes on Economic Growth in Nigeria. World Journal of Finance and Investment Research, 1(1), pp. 44-52.

Irizepova, M.S. (2016). Capabilities of correlation-regression analysis for forecasting of Value-Added Tax. Mediterranean Journal of Social Sciences, 7(1), pp. 24-48

Jack, W. (1996).The efficiency of VAT implementation: A comparative study of Central and Eastern European countries in transition. IMF working paper, WP/96/79.

Keen, M.M. (2013).The Anatomy of the VAT (No. 13-111). International Monetary Fund.

Koirala, T.P. (2011). Government revenue forecasting in Nepal. NBR Economic Review, 4(2).

Kong, D. (2007). Local Government Revenue forecasting: The California county Experience. Journal of Public Budgeting, Accounting & Financial Management, 19(2), pp. 178-199.

Le Minh, T., Jensen, L., Shukla, G.P., & Biletska, N. (2016).Assessing domestic revenue mobilization: analytical tools and techniques. World Bank Group. Macroeconomic and Fiscal Management Discussion Paper, 15, pp. 1-50

Makananisa, M.P. (2015). Forecasting annual tax revenue of the South African taxes using time series Holt-Winters and ARIMA/SARIMA Models (Doctoral dissertation). Department of Statistics, University of South Africa.

Makridakis, S., Wheelwright, S.C., & Hyndman, R.J. (2008). Forecasting methods and applications. John Wiley & Sons, Hoboken, New Jersey.

Narayan, P.K. (2003). The macroeconomic impact of the IMF recommended VAT policy for the Fiji economy: evidence from a Computational General Equilibrium model. Review of Urban & Regional Development Studies, 15(3), pp. 226-236.

Nartey, D.A. (2011).Effects of changes in the rates of Value Added Tax on VAT revenue in Ghana from 2003 to 2010. Department of Business Administration, Kwame Nkrumah University of Science and Technology.

Nazmi, N., & Leuthold, J.H. (1988). Forecasting economic time series that require a power transformation: Case of state tax receipts. Journal of Forecasting, 7(3), pp. 173-184.

Nikolov, M. (2002). Tax revenue forecasting with intervention time series modelling. Bulletin, Ministry of Finance, Republic of Macedonia.

Pegels, C.C. (1969). Exponential forecasting: some new variations. Management Science, 12(5), pp. 311–315.

Slobodnitsky, T., & Drucker, L. (2008). VAT revenue forecasting in Israel. Ministry of Finance, State Revenue Administration, The Maurice Falk Institute for Economic Research in Israel Ltd., Digest, (10).

Sokolovska, O., & Sokolovskyi, D. (2015). VAT efficiency in countries worldwide. Research Institute of Financial Law, State Fiscal Service of Ukraine.

Sologoub, D., & Legeida, N. (2003). Modelling value-added tax (VAT) revenues in a transition economy: Case of Ukraine. Institute for economic research and policy consulting working paper, 22, pp. 1-21.




Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.