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FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs

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  • Rangan Gupta

Abstract

The paper develops a Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1-2000:4 and forecasts GDP, consumption, investment, short and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out-of-sample-forecast accuracy resulting from the BVECM is compared with those generated from the Classical variant of the VAR and VECM and the Bayesian VAR. The BVECM is found to produce the most accurate out of sample forecasts. It also correctly predicts the direction of change in the chosen macroeconomic indicators. Copyright (c) 2006 The Author. Journal compilation (c) 2006 Economic Society of South Africa.

Suggested Citation

  • Rangan Gupta, 2006. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 611-628, December.
  • Handle: RePEc:bla:sajeco:v:74:y:2006:i:4:p:611-628
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    References listed on IDEAS

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    1. Pami Dua & Anirvan Banerji & Stephen M. Miller, 2006. "Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 415-437.
    2. William C. Gruben & William T. Long, 1988. "Forecasting the Texas economy: applications and evaluation of a systematic multivariate time series model," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Jan, pages 11-28.
    3. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    4. H. Smith & J.n. Blignaut & J.h. Van heerden, 2006. "An Analysis Of Inventory Investment In South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 74(1), pages 6-19, March.
    5. William C. Gruben & William T. Long, III, 1988. "The New Mexico economy: outlook for 1989," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Nov, pages 21-36.
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    Cited by:

    1. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    2. Rangan Gupta & Alain Kabundi, 2010. "Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
    3. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
    4. Guangling ‘Dave’ Liu & Rangan Gupta & Eric Schaling, 2007. "Forecasting the South African Economy: A DSGE-VAR Approach," Working Papers 51, Economic Research Southern Africa.
    5. Guangling 'Dave' Liu & Rangan Gupta & Eric Schaling, 2009. "A New-Keynesian DSGE model for forecasting the South African economy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 387-404.
    6. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    7. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.
    8. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States," Working Papers 200912, University of Pretoria, Department of Economics.
    9. 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.
    10. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    11. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    12. Balcilar, Mehmet & Gupta, Rangan & Shah, Zahra B., 2011. "An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa," Economic Modelling, Elsevier, vol. 28(3), pages 891-899, May.
    13. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    14. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00505165, HAL.
    15. Rangan Gupta & Stephen Miller, 2012. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 339-361, April.
    16. repec:hal:journl:halshs-00511979 is not listed on IDEAS
    17. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, March.
    18. 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.

    More about this item

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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