Skip to content
Research Article Open access CC BY 4.0

On the Revenue Generation of the Internally Generated Funds across Twifo Hemang Lower Denkyira District Assembly

Bridget Sena Borbor, Joshua Kwaku Hodinyah, Ali Abubakar

Asian Journal of Probability and Statistics · pp. 299–306 · Published 21 Dec 2021

10.9734/ajpas/2021/v15i430380

Abstract

Balancing the budget is one of the most important concerns of financial policy. Improving the quality of revenue and expenditure projections has become essential for policymakers. However, The most crucial component in sustaining success in terms of revenue generation and other grounds is time. Keeping up with the speed of time is difficult. A time series model is one such method for dealing with time-based data. The time series model is an adequate model when there are serially correlated data. Autoregressive Moving Averages (ARMA) is the appropriate approach when the error(s) of the data has the same variance regardless of the value taken by the independent variable(s).  For this reason, an internally generated fund data were collected from the Twifo Hemang Lower Denkyira District assembly from 2013 to 2019 which was subjected to descriptives and time series analysis. From the time series analysis, ARMA (1, 1) was selected as the best model using the AIC value and fit the observed monthly internally generated fund pattern. The study revealed among others that January 2020 will record the highest revenue generation of 21465.96 cedis over the two years forecast followed by March 2020, 19023.17 cedis  and May 2021 of 18122.05 cedis. The study also recommended among others that the authorities of Twifo Hemang Lower Denkyira District assembly should embark on educating the citizens on the need to pay their taxes for developmental progress of their assembly.

Revenue internally generated funds homoscedasticity ARMA time series.

Cited by 0

No indexed citations yet.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

0

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.