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Forecasting the South African economy: a hybrid-DSGE approach

Author

Listed:
  • 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.

Suggested Citation

  • Guangling “Dave” Liu & Rangan Gupta & Eric Schaling, 2010. "Forecasting the South African economy: a hybrid-DSGE approach," Journal of Economic Studies, Emerald Group Publishing, vol. 37(2), pages 181-195, May.
  • Handle: RePEc:eme:jespps:v:37:y:2010:i:2:p:181-195
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    Citations

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    Cited by:

    1. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    2. 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.
    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. Rangan Gupta & Patrick T. Kanda & Mampho P. Modise & Alessia Paccagnini, 2015. "DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa," Applied Economics, Taylor & Francis Journals, vol. 47(3), pages 207-221, January.
    5. Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
    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. Alessia Paccagnini, 2012. "Comparing Hybrid DSGE Models," Working Papers 228, University of Milano-Bicocca, Department of Economics, revised Dec 2012.
    8. Annari De Waal & Reneé Van Eyden & Rangan Gupta, 2015. "Do we need a global VAR model to forecast inflation and output in South Africa?," Applied Economics, Taylor & Francis Journals, vol. 47(25), pages 2649-2670, May.
    9. 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.
    10. repec:ipg:wpaper:2014-562 is not listed on IDEAS
    11. 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.

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