Abstract:
This study determines whether the global vector autoregressive (GVAR)
approach provides better forecasts of key South African variables than a
vector error correction model (VECM) and a Bayesian vector autoregressive
(BVAR) model augmented with foreign variables. The article considers
both a small GVAR model and a large GVAR model in determining
the most appropriate model for forecasting South African variables. We
compare the recursive out-of-sample forecasts for South African GDP and
inflation from six types of models: a general 33 country (large) GVAR, a
customized small GVAR for South Africa, a VECM for South Africa with
weakly exogenous foreign variables, a BVAR model, autoregressive (AR)
models and random walk models. The results show that the forecast
performance of the large GVAR is generally superior to the performance
of the customized small GVAR for South Africa. The forecasts of both the
GVAR models tend to be better than the forecasts of the augmented
VECM, especially at longer forecast horizons. Importantly, however, on
average, the BVAR model performs the best when it comes to forecasting
output, while the AR(1) model outperforms all the other models in predicting
inflation. We also conduct ex ante forecasts from the BVAR and
AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track
turning points in output and inflation, respectively.
