IDEAS home Printed from https://ideas.repec.org/a/ags/afjecr/264561.html
   My bibliography  Save this article

Forecasting Tax Revenue and its Volatility in Tanzania

Author

Listed:
  • Chimilila, Cyril

Abstract

Forecasting tax revenue and its predictability is important for government budgeting and tax administration purposes. This study used monthly tax revenue data for a period of 182 months spanning January 2000 to February 2015. The study applied ARMA and combined forecast models, and GARCH models to forecast tax revenue and its volatility, respectively. Tax revenue was found to increase steady over the period, although with a persistent volatility which increases over time. The observed volatility was found to be associated with taxes from bases (income) which have high volatility. Based on various forecast accuracy evaluation criteria, the study recommends combined forecasts and GARCH(1,1) models for forecasting monthly revenue and its volatility, respectively. The study further recommends enhanced diversity of taxes through widening consumption tax base within the existing tax portfolio so as to enhance its contribution to revenue collection and reduce volatility.

Suggested Citation

  • Chimilila, Cyril, 2017. "Forecasting Tax Revenue and its Volatility in Tanzania," African Journal of Economic Review, African Journal of Economic Review, vol. 5(01), January.
    • Handle: RePEc:ags:afjecr:264561
    DOI: 10.22004/ag.econ.264561
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/264561/files/149255-392766-1-SM.pdf
    Download Restriction: no
    File URL: https://ageconsearch.umn.edu/record/264561/files/149255-392766-1-SM.pdf?subformat=pdfa
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.264561?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bunn, Derek W., 1985. "Statistical efficiency in the linear combination of forecasts," International Journal of Forecasting, Elsevier, vol. 1(2), pages 151-163.
    2. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    3. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
    4. Teresa Leal & Javier J. Pérez & Mika Tujula & Jean-Pierre Vidal, 2008. "Fiscal Forecasting: Lessons from the Literature and Challenges," Fiscal Studies, Institute for Fiscal Studies, vol. 29(3), pages 347-386, September.
    5. Auerbach, Alan J, 1995. "Tax Projections and the Budget: Lessons from the 1980's," American Economic Review, American Economic Association, vol. 85(2), pages 165-169, May.
    6. Glenn Jenkins & CHUN-YAN KUO & GANGADHAR SHUKLA, 2000. "Tax Analysis and Revenue Forecasting," Development Discussion Papers 2000-05, JDI Executive Programs.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Bretschneider, Stuart I. & Gorr, Wilpen L. & Grizzle, Gloria & Klay, Earle, 1989. "Political and organizational influences on the accuracy of forecasting state government revenues," International Journal of Forecasting, Elsevier, vol. 5(3), pages 307-319.
    9. Gary C. Cornia & Ray D. Nelson, 2010. "State tax revenue growth and volatility," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Oct, pages 23-58.
    10. Aksu, Celal & Gunter, Sevket I., 1992. "An empirical analysis of the accuracy of SA, OLS, ERLS and NRLS combination forecasts," International Journal of Forecasting, Elsevier, vol. 8(1), pages 27-43, June.
    11. 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).
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khatibu Kazungu & John R. Mboya, 2021. "Volatility of Stock Prices in Tanzania: Application of Garch Models to Dar Es Salaam Stock Exchange," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 9(1), pages 15-28, March.
    2. Yılmaz, Engin, 2018. "Vergi gelirlerinin tahminlenmesine yönelik ekonometrik model [Econometric model for forecasting tax revenues]," MPRA Paper 91192, University Library of Munich, Germany, revised 01 Dec 2018.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Konstantinos Petridis & Nikolaos E. Petridis & Fouad Ben Abdelaziz & Hatem Masri, 2023. "Ranking econometric techniques using geometrical Benefit of Doubt," Annals of Operations Research, Springer, vol. 330(1), pages 411-430, November.
    4. Nageri Kamaldeen Ibraheem, 2019. "Evaluating Good and Bad News During Pre and Post Financial Meltdown: Nigerian Stock Market Evidence," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 64(3), pages 1-22, December.
    5. Shekar Bose & Hafizur Rahman, 2022. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market," SAGE Open, , vol. 12(4), pages 21582440221, October.
    6. Nico Knuth & Andreas Nastansky, 2025. "Anwendung von Deep Learning in der Prognose der Volatilität des DAX: Ein Vergleich der Prognosegüte von GARCH und LSTM," Statistische Diskussionsbeiträge 59, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät.
    7. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Mean and variance causality between the Cyprus Stock Exchange and major equity markets," Working Papers 0501, University of Crete, Department of Economics.
    8. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    9. McMillan, David G. & Speight, Alan E. H., 2001. "Non-ferrous metals price volatility: a component analysis," Resources Policy, Elsevier, vol. 27(3), pages 199-207, September.
    10. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    11. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    12. Mailand, Wilhelm, 1998. "Zum Einfluß von Unsicherheit auf die gesamtwirtschaftliche Investitionstätigkeit," HWWA Discussion Papers 57, Hamburg Institute of International Economics (HWWA).
    13. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    14. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    15. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    16. Kanungo, Rama Prasad, 2021. "Uncertainty of M&As under asymmetric estimation," Journal of Business Research, Elsevier, vol. 122(C), pages 774-793.
    17. David McMillan & Alan Speight, 2006. "Heterogeneous information flows and intra-day volatility dynamics: evidence from the UK FTSE-100 stock index futures market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(13), pages 959-972.
    18. Jiangze Du & Shaojie Lai & Kin Keung Lai & Shifei Zhou, 2021. "A novel term structure stochastic model with adaptive correlation for trend analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5485-5498, October.
    19. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.
    20. Verbeek, Marno, 2007. "A Guide to Modern Econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 8(4), pages 125-132.

    More about this item

    Keywords

    Financial Economics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:afjecr:264561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://www.ajol.info/index.php/ajer/index .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.