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(1), 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. 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.
    4. Shekar Bose & Hafizur Rahman, 2022. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market," SAGE Open, , vol. 12(4), pages 21582440221, October.
    5. Dankenbring, Henning, 1998. "Volatility estimates of the short term interest rate with an application to German data," SFB 373 Discussion Papers 1998,96, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    6. Pierdzioch, Christian, 2000. "Noise Traders? Trigger Rates, FX Options, and Smiles," Kiel Working Papers 970, Kiel Institute for the World Economy (IfW Kiel).
    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. W. K. Li & Shiqing Ling & Michael McAleer, 2001. "A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors," ISER Discussion Paper 0545, Institute of Social and Economic Research, Osaka University.
    9. 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.
    10. Raúl de Jesús Gutiérrez & Edgar Ortiz & Oswaldo García Salgado, 2017. "Long-term effects of the asymmetry and persistence of the prediction of volatility: Evidence for the equity markets of Latin America," Contaduría y Administración, Accounting and Management, vol. 62(4), pages 1081-1099, Octubre-D.
    11. 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.
    12. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    13. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    14. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. 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.
    17. Zidong An & Joao Tovar Jalles, 2020. "On the performance of US fiscal forecasts: government vs. private information," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(2), pages 367-391, June.
    18. Mahua Barari & Nityananda Sarkar & Srikanta Kundu & Kushal Banik Chowdhury, 2014. "Forecasting House Prices in the United States with Multiple Structural Breaks," International Econometric Review (IER), Econometric Research Association, vol. 6(1), pages 1-23, April.
    19. 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.
    20. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    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.