The passage of National Health Insurance Scheme to replace the old system (called cash and carry) in Ghana seems to have raised many questions as to whether it has increased the rate at which people attend hospital and abolished cash and carry system. The data collected were hospital attendance for both health insurance and cash and carry system on monthly basis across age groups and gender for 2008-2017, obtained from Cape Coast Teaching Hospital. Chi-Square tests and the Box-Jenkins's methodology of time series analysis were employed to analyse the data. From the findings, the autocorrelation function (ACF) and partial autocorrelation function (PACF) plot suggested an AR process with order 1. Candidate models were obtained using the minimum AIC criteria to select adequate models and appropriate models were obtained as SARIMA (1,0,0) (0,1,0) 12 model for insured (NHIS) and SARIMA (1,1,1) (2,0,1) 12 model for uninsured (Cash and Carry system). Model diagnostics tests were performed using Ljung-Box test. The Chi-square tests inferred dependence in hospital attendance between insured and non-insured patients on gender and the years, In conclusion, insured patients will be increasing throughout the age groups and non-insured patients will be increasing for specific age groups 0-28 days to 15-17 years for the next 24 months. This research recommended among others that education should be given to the general public about the importance of health insurance, it registration and operations especially age group 0-28days to 15-17 years because they seem to continue the use of Cash and Carry System in seeking healthcare regardless of the introduction of NHIS.
Aims: Outpatient department is one of the first points of contact for patients accessing health care and provide patients with their primary healthcare as they seek services at the facility. With the introduction of community-based health planning and services, there seems that the outpatient departments have witnessed corresponding progressive and significant increase in attendance at the various health facilities in Ghana of which the research seeks to investigate. Materials and Methods: The data collected were outpatient hospital attendance of patients on a monthly basis from 2012 to 2019 obtained from the Cape Coast Teaching Hospital. Box Jenkins’s methodology of time series analysis was used to analyse the data. The modified Box Pierce (Ljumg-Box) Chi-square statistic criteria of the largest and minimum Chi-square statistic value was in selecting the best fitted model for outpatient department attendance. Results: The autocorrelation function (ACF) and partial autocorrelation function (PACF) plots suggested an autoregressive (AR) process with order 2 and moving average (MA) process with order 1 which was used in selecting the appropriate model. Candidate models were obtained using the lowest Chi-square value and highest value to select adequate models and the best model. The best non-seasonal model for the data was ARIMA (2, 2, 1) for the outpatient department attendance. Model diagnostics test was performed using Ljung-Box test. Conclusion: The findings of the forecast showed that OPD visits will increase in the next five years. Specifically, continued use of the outpatient department in accessing health care at all levels will experience an increase in hospital visits across the months from June 2020 to December 2025. Recommendations from this research included among others that, the health authorities should continue to expand the outpatient department services to increase access to healthcare by all as it services goes to the core people in the community.
This paper analyses Ghana’s gross domestic product using time series Autoregressive Integrated Moving Average (ARIMA). Time series analysis involves the application of statistical models to time series data and is useful for analysing the dynamics of Gross domestic product. The Ghana’s Gross domestic products (GDP) from 1980 to 2020 were obtained from the International Monetary Fund (IMF) datasets. Box Jenkins’s methodology of time series analysis was employed to analyse the data. The autocorrelation function (ACF) and partial autocorrelation function (PACF) plot suggested an Autoregression of order one AR(1). The (ARIMA) models were obtained using the minimum AIC criteria. Model diagnostics tests were performed using Ljung-Box test. The paper established that Ghana’s GDP will incline throughout the period of 2021-2025.
Unemployment is one of the major socioeconomic issues across the globe in which Ghana is no exception. The unavailability of jobs and its creation as being searched by persons belonging to the labour force actively looking for jobs makes the problem escalate rapidly in a growing economy. In this seven state model, we analysed into the three main economic sectors of Ghana to investigate how unemployment, employment, and newly created vacancy creation behave at equilibrium on the three economic sector levels. Moreover, we analysed how in a specific sector, the dynamics of the state variables control unemployment. Further analyses on the parameters indicated that, an increase in the rate of newly created vacancies results in a decrease in the number of unemployed persons and an increase in the number of employed. We assumed that a jobless person who is available for work but fails to make an effort to seek work is not part of the unemployed class among others. It was established that the model has one nonnegative equilibrium point. Lastly, we analyse the impact of perturbation of some parameters on the number of the unemployed and employed persons at equilibrium.unemployment
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.
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