Abstract:
This paper analyzes how the information contained in the disaggregate components
of aggregate inflation helps improve the forecasts of the aggregate series. Direct
univariate forecasting of the aggregate inflation data by an autoregressive (AR)
model is used as the benchmark with which all autoregressive (AR), moving average
(MA) and vector autoregressive (VAR) models of the disaggregates are compared.
The results show that directly forecasting the aggregate series from the benchmark
model is generally superior to aggregating forecasts from the disaggregate
components. Additionally, including information from the disaggregates in the
aggregate model rather than aggregating forecasts from the disaggregates performs
best in all forecast horizons when appropriate disaggregates are used. The
implication of these results is that better inflation forecasts for Ghana are produce
by using information from relevant disaggregates in the aggregate model rather than
direct forecasts of the aggregate or aggregating forecasts from the disaggregates.