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Forecasting agricultural exports and imports in South Africa

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  • J. M. Kargbo

Abstract

The implementation of wide-ranging policy reforms, including trade and exchange rate policies, is improving the efficiency of the South African economy and its reintegration into the global economy with rapid export expansion. Agricultural exports in the Southern African Customs Union increased from R8.14 billion in 1995 to R23.0 billion in 2003, whilst agricultural imports rose from R6.83 billion to R13.84 billion during the same period. This article uses alternative approaches to forecasting agricultural exports and imports in South Africa. The models used include: exponential smoothing, autoregressive integrated moving average (ARIMA), vector autoregression (VAR), Engle-Granger (EG) single-equation and vector error-correction models (VECM). We found that the ARIMA and EG methods outperform the VAR and VECM according to Theil's U-statistic. The VAR outperforms the VECM in forecasting agricultural exports in South Africa. The combined forecasts have a lower variance compared to individual forecasts, thereby, reducing the risks of making wrong decisions based on the forecasts. The article provides empirical evidence that is beneficial to policymakers and business leaders in South Africa as they strive to reduce poverty and inequality and increase economic growth.

Suggested Citation

  • J. M. Kargbo, 2007. "Forecasting agricultural exports and imports in South Africa," Applied Economics, Taylor & Francis Journals, vol. 39(16), pages 2069-2084.
    • Handle: RePEc:taf:applec:v:39:y:2007:i:16:p:2069-2084
    DOI: 10.1080/00036840600707183
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    References listed on IDEAS

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    1. Madhavi Bokil & Axel Schimmelpfennig, 2006. "Three Attempts at Inflation Forecasting in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(3), pages 341-368.
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    4. 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, April.
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    Cited by:

    1. Sara Rafiq & Liu Hai Yun & Gulzar Ali, 2016. "Forecasting the Trend Analysis of Trade Balance of Pakistan: A Theoretical and Empirical Investigation," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 6(7), pages 188-214, July.

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