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Do we need a global VAR model to forecast inflation and output in South Africa?

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Author Info

  • Annari de Waal

    ()
    (Department of Economics, University of Pretoria)

  • Renee van Eyden

    ()
    (Department of Economics, University of Pretoria)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

Abstract

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The paper considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33-country (large) GVAR, a customised small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customised small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1-2012:Q4, to highlight their ability to track turning points in output and inflation respectively.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201346.

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Length: 34 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:pre:wpaper:201346

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Keywords: South Africa; global vector autoregressive (GVAR) model; Bayesian vector autoregressive (BVAR) model; forecasting;

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References

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  1. Dees, S. & di Mauro, F. & Pesaran, M.H. & Smith, L.V., 2005. "Exploring the International Linkages of the Euro Area: a Global VAR Analysis," Cambridge Working Papers in Economics 0518, Faculty of Economics, University of Cambridge.
  2. Guangling (Dave) Liu & Rangan Gupta, 2006. "A Small-Scale DSGE Model for Forecasting the South African Economy," Working Papers 200621, University of Pretoria, Department of Economics.
  3. Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2013. "Forecasting South African Macroeconomic Data with a Nonlinear DSGE Model," Working Papers 201313, University of Pretoria, Department of Economics.
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  5. Guangling (Dave) Liu & Rangan Gupta & Eric Schaling, 2008. "A New-Keynesian DSGE Model for Forecasting the South African Economy," Working Papers 200805, University of Pretoria, Department of Economics.
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  19. Annari de Waal & Renee van Eyden, 2013. "The impact of economic shocks in the rest of the world on South Africa: Evidence from a global VAR," Working Papers 201328, University of Pretoria, Department of Economics.
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