Abstract:
Revenue data structure has assumed a dynamic nature, and evolving methodology
for their study constitutes an interesting problem. In this regard, the study
examines the various revenue components that are most influential in revenue
generation and attempts to obtain a suitable multivariate time series model that
characterizes the contribution of each revenue component in Ghana. Data is
therefore obtained on some fourteen revenue variables from Ghana Community
Management System for the study. The theory of VEC modelling, which is
relevant for variables expected to be related in the long nm, is found appropriate
for the study. An optimum lag order is determined at 8. The VEC(8) model
produces more realistic performance measures than the initial V AR(8). By
incorporating principal components extraction into the VEC model, five salient
revenue dimensions are identified with no loss of information. The most dominant
source is what is influenced by eIF, accounting for about 80% of the total
variation in all revenue sources. The remaining 20% is explained by Volume
(VOL), Total Revenue (TORE), Total Amount Exempt (TOAE) and Petroleum
tax (PETAL), in that order. The VEC model is applied to project the original data
onto the five components. The resulting PCA VEC model now provides a
plausible econometric characterization of the data structure. The results suggest
that CIF, in particular, should be protected to generate the requisite revenue.
