Predicting Insurance Penetration Rate in Ghana Using the Autoregressive Integrated Moving Average (ARIMA) Model
Ghana records a low penetration of 1.05% compared to some of its African counterparts. For example South Africa, which has an insurance penetration rate of 17%, followed by Namibia which records 6.3%. This means, there is more room for improvement. More upsetting, with Ghana hovering around the 1% as at 2018, the rate works out to the small amount of Gross Domestic Product. This research seeks to model and forecast insurance penetration rate in Ghana using the Autoregressive Integrated Moving Average technique. The result indicates that ARIMA (3,1,0) is the appropriate model for insurance penetration in Ghana. Also, results from the forecast could serve as an advisory or the need to re-strategize as a country. Therefore, determining the future pattern of insurance penetration will lead to the remedies that will increase the number of insured in the future.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Selection and Behavioral Responses of Health Insurance Subsidies in the Long Run: Evidence from a Field Experiment in Ghana
We conduct a randomized experiment that varies one-time health insurance subsidy amounts (partial and full) in Ghana to study the impacts of subsidies on insurance enrollment and health care utilization. We find that bot…
Sensitivity Analysis of Ruin of an Insurance Company in Ghana
An insurance company, as a risk bearer, is exposed to the likelihood of running into ruin. This is the situation where the initial surplus falls below zero. There is the need to find the required start-up capital to hedg…
SensitivityImplementing agile healthcare frame works in the context of low income countries: Proposed Framework and Review
Agile healthcare frameworks, derived from methodologies in IT and manufacturing, offer transformative potential for low-income regions. This study explores Agile integration in resource-constrained environments, focusing…
Decision MakingManagementDeterminants of Longevity amongst SSNIT Pensioners in Ghana
This paper statistically analysed pensioner longevity in Ghana. It fundamentally sought to ascertain the significant determinants of longevity amongst Ghanaian pensioners, specifically, SSNIT pensioners by estimating the…
Predicting House Rental Prices in Ghana Using Machine Learning
This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset o…
Feature Importance