Abstract:
This paper considers whether the use of real oil price data can improve
upon the forecasts of the interest rate in South Africa. We employ various
Bayesian vector autoregressive (BVAR) models that make use of various
measures of oil prices and compare the forecasting results of these models
with those that do not make use of this data. The real oil price data is also
disaggregated into positive and negative components to establish whether
this would improve upon the forecasting performance of the model. The
full dataset includes quarterly measure of output, consumer prices, ex-
change rates, interest rates and oil prices, where the initial in-sample
extends from 1979q1 to 1997q4. We then perform rolling estimations
and forecasts over the out-of-sample period 1998q1 to 2014q4, after the
in-sample period is extended to incorporate an additional observation.
The results suggest that models that include information relating to oil
prices outperform the model that does not include this information, when
comparing their out-of-sample forecasts. In addition, the model with the
positive component of oil price tends to perform better than other mod-
els at the short- to medium-run horizons. Then lastly, the model that
includes both the positive and negative components of the oil price, pro-
vides superior forecasts at longer horizons, where the improvement is large
enough to ensure that it is the best forecasting model on average. Hence,
not only do real oil prices matter when forecasting interest rates, but the
use of disaggregate oil price data may facilitate additional improvements.
