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A Comparison of Multi-step GDP Forecasts for South Africa

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  • Guillaume Chevillon

Abstract

To forecast at several, say h, periods into the future, a modeller faces two techniques: iterating one-step ahead forecasts (the IMS technique) or directly modeling the relation betwen observations separated by an h-period interval and using it for forecasting (DMS forecasting). It is known that structural breaks, unit-root non-stationarity and residual autocorrelation benefit DMS accuracy in finite samples, all of which occuring when modelling the South African GDP over the last thirty years. This paper analyzes the forecasting properties of the model developed by Aron and Muellbauer (2002) and compares with them that of 30 derived or competing models. We find that the GDP of South Africa is best forecast, 4 quarters ahead, using the technique developed by these authors and its variants as derived in the present paper. Rankings of other models vary over time and it is difficult to recommend one of them as a rule in this exercise.

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 212.

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Date of creation: 01 Dec 2004
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Handle: RePEc:oxf:wpaper:212

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Keywords: Multi-step Forecasting; Structural Breaks; Forecast Comparisons;

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References

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  1. Guillaume Chevillon, 2004. "`Weak` trends for inference and forecasting in finite samples," Economics Series Working Papers 210, University of Oxford, Department of Economics.
  2. Aron, Janine & Muellbauer, John, 2002. "Interest Rate Effects on Output: Evidence from a GDP Forecasting Model for South Africa," CEPR Discussion Papers 3595, C.E.P.R. Discussion Papers.
  3. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  4. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
  5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  6. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, December.
  7. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-47, July-Sept.
  8. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
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