The paper uses the Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error
Correction Model (BVECM) of the South African economy for the period 1970:1-2000:4, and
then forecasts GDP, consumption, investment, short and long term interest rates, and the CPI over
the period of 2001:1 to 2005:4. We find that a tight prior produces relatively more accurate
forecasts than a loose one. The out-of-sample-forecast accuracy resulting from the Gibbs sampled
BVECM is compared with those generated from a Classical VECM and a homoscedastic BVECM.
The homoscedastic BVECM is found to produce the most accurate out of sample forecasts.