IDEAS home Printed from https://ideas.repec.org/p/sza/wpaper/wpapers199.html
   My bibliography  Save this paper

Comparing the BER’s forecasts

Author

Listed:
  • Nicolaas van der Wath

    (Bureau for Economic Research)

Abstract

The Bureau for Economic Research publishes annual (and quarterly) forecasts for more than 140 macroeconomic indicators, with a forecasting horizon stretching up to 6 years ahead. These forecasts are generated with the aid of a structural macro-econometric model of the South African economy. The purpose of this re-search note is to test the accuracy of the BER’s forecasts. Also to compare them with other published forecasts according to accuracy, forecast horizon and number of indicators. To determine the level of accuracy, we have calculated the mean absolute errors and the root mean squared errors of the BER’s forecasts for a selection of five economic indicators. These statistics were also calculated for the forecasts of the selected other institutions or models. From these the relative accuracy of the different forecasts were compared to each other and ranked ac-cordingly. The consensus forecast turned out to be the most accurate for the im-mediate year, followed with a narrow margin by the BER. The close proximity of these two forecasts is striking. Other conclusions are that structural forecasting models perform better than mechanical ones for the first two years, but lose their accuracy advantage from the third or fourth year onwards. They also fail to antic-ipate critical turning points in economic cycles.

Suggested Citation

  • Nicolaas van der Wath, 2013. "Comparing the BER’s forecasts," Working Papers 23/2013, Stellenbosch University, Department of Economics.
  • Handle: RePEc:sza:wpaper:wpapers199
    as

    Download full text from publisher

    File URL: https://www.ekon.sun.ac.za/wpapers/2013/wp232013/wp-23-2013.pdf
    File Function: First version, 2013
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Philip Hans Franses & Rianne Legerstee & Richard Paap, 2017. "Estimating loss functions of experts," Applied Economics, Taylor & Francis Journals, vol. 49(4), pages 386-396, January.
    2. David Mortimer Krainz, 2011. "An Evaluation of the Forecasting Performance of Three Econometric Models for the Eurozone and the USA," WIFO Working Papers 399, WIFO.
    3. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    4. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    5. Jan Kmenta & James B. Ramsey, 1980. "Evaluation of Econometric Models," NBER Books, National Bureau of Economic Research, Inc, number kmen80-1, March.
    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. Nicolaas van der Wath, 2016. "Gauging financial conditions in South Africa," Working Papers 10/2016, Stellenbosch University, Department of Economics.

    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. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    2. Arie Preminger & Uri Ben-zion & David Wettstein, 2007. "The extended switching regression model: allowing for multiple latent state variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 457-473.
    3. repec:lan:wpaper:470 is not listed on IDEAS
    4. Ericsson, Neil R., 1992. "Parameter constancy, mean square forecast errors, and measuring forecast performance: An exposition, extensions, and illustration," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 465-495, August.
    5. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    6. Alejandro Parot & Kevin Michell & Werner D. Kristjanpoller, 2019. "Using Artificial Neural Networks to forecast Exchange Rate, including VAR‐VECM residual analysis and prediction linear combination," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(1), pages 3-15, January.
    7. Pablo Pincheira, 2006. "Shrinkage Based Tests of the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 376, Central Bank of Chile.
    8. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
    9. 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.
    10. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving forecasting performance by window and model averaging," CAMA Working Papers 2011-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Francis X. Diebold & Roberto S. Mariano, 1991. "Comparing predictive accuracy I: an asymptotic test," Discussion Paper / Institute for Empirical Macroeconomics 52, Federal Reserve Bank of Minneapolis.
    12. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
    13. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    14. Garratt, Anthony & Lee, Kevin, 2010. "Investing under model uncertainty: Decision based evaluation of exchange rate forecasts in the US, UK and Japan," Journal of International Money and Finance, Elsevier, vol. 29(3), pages 403-422, April.
    15. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    16. repec:lan:wpaper:425 is not listed on IDEAS
    17. repec:lan:wpaper:539557 is not listed on IDEAS
    18. repec:lan:wpaper:413 is not listed on IDEAS
    19. Arie Preminger & Uri Ben-Zion & David Wettstein, 2006. "Extended switching regression models with time-varying probabilities for combining forecasts," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 455-472.
    20. Fildes, Robert, 2006. "The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion," International Journal of Forecasting, Elsevier, vol. 22(3), pages 415-432.
    21. Joseph Zhi Bin Ling & Albert K. Tsui & Zhaoyong Zhang, 2021. "Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    22. Stefan Mittnik & Nikolay Robinzonov & Klaus Wohlrabe, 2013. "The Micro Dynamics of Macro Announcements," CESifo Working Paper Series 4421, CESifo.
    23. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    24. Gürkaynak, Refet S. & Kısacıkoğlu, Burçin & Lee, Sang Seok, 2022. "Exchange rate and inflation under weak monetary policy: Turkey verifies theory," CFS Working Paper Series 679, Center for Financial Studies (CFS).

    More about this item

    Keywords

    forecast comparison; forecast accuracy; forecast evaluation; consen-sus forecast; Theil coefficients; mean absolute error; root mean squared error; loss function;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sza:wpaper:wpapers199. 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: Melt van Schoor (email available below). General contact details of provider: https://edirc.repec.org/data/desunza.html .

    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.