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Forecasting the South African economy: a hybrid-DSGE approach

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Author Info

  • Guangling “Dave” Liu
  • Rangan Gupta
  • Eric Schaling

Abstract

Purpose – This paper aims to develops an estimable hybrid model that combines the micro-founded DSGE model with the flexibility of the atheoretical VAR model. Design/methodology/approach – The model is estimated via the maximum likelihood technique based on quarterly data on real gross national product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive estimation using the Kalman filter algorithm, the out-of-sample forecasts from the hybrid model are then compared with the forecasts generated from the Classical and Bayesian variants of the VAR for the period 2001:1-2005:4. Findings – The results indicate that, in general, the estimated hybrid-DSGE model outperforms the classical VAR, but not the Bayesian VARs in terms of out-of-sample forecasting performances. Research limitations/implications – The model lacks nominal shocks and needs to be extended into a small open economy framework. Practical implications – The paper was able to show that, even though the DSGE model is outperformed by the BVAR, a microfounded theoretical DSGE model has a future in forecasting the South African economy. Originality/value – To the best of the authors' knowledge, this is the first attempt to use an estimable DSGE model to forecast the South African economy.

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Bibliographic Info

Article provided by Emerald Group Publishing in its journal Journal of Economic Studies.

Volume (Year): 37 (2010)
Issue (Month): 2 (May)
Pages: 181-195

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Handle: RePEc:eme:jespps:v:37:y:2010:i:2:p:181-195

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Web page: http://www.emeraldinsight.com

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Related research

Keywords: Economics; Forecasting; National economy; South Africa;

References

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Citations

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Cited by:
  1. Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
  2. Rangan Gupta &  Patrick Kanda & Mampho Modise & Alessia Paccagnini, 2013. "DGSE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa," Working Papers 259, University of Milano-Bicocca, Department of Economics, revised Nov 2013.
  3. Gupta, Rangan & Kabundi, Alain, 2011. "A large factor model for forecasting macroeconomic variables in South Africa," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
  4. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5590845, Tilburg University.
  5. Annari de Waal & Renee van Eyden, 2013. "Forecasting key South African variables with a global VAR model," Working Papers 201346, University of Pretoria, Department of Economics.
  6. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
  7. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.

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