Through the years, health expenditures have been increasing among developed and developing coun-tries. Specifically, total health expenditure in Rwanda, has experienced an increasing trend, for exam- plea the total health expenditure in Rwanda was 282929; 329526 and 324385 million Rwandan francs in 2016 ; 2017 and 2018 respectively and the rise in healthcare expenditures need a rational and equity management to better provide health services efficiently and effectively. The ARIMA model has been the preferred model to forecast health expenditures for decades which later have been criticized to not capture the non-linear behavior in data. Moreover, two decades ago, the artificial neural networks (ANNs) models have got attention and improved the forecasting accuracy. However, it has been found that each of the two models has weaknesses when linear and nonlinear Behaviors are in the data, thus each of the ARIMA and ANN is no longer appropriate to model the series. Since the ARIMA model only performs better in capturing linear behavior and ANN is good in capturing nonlinear behavior in data. It is with this background that, we proposed a hybrid model, which differs in combining the advantages of ARIMA and ANNs in capturing the linear and non-linear relationship in data. The ARIMA-ANNs model was tested on sets of health expenditure actual data. Our results showed the effectiveness of the hybrid model which has a higher prediction accuracy as compared to the existing models. Because of the fact, the empirical results from all of the three models considered in this study showed that from 2006 looking forward to 2027, there will be an increasing trend of health expenditure. The results showed that the forecasted values of health expenditure by ARIMA models will be 557,299.97 million Rwandan francs in 2027. While using the ANNs models, the forecasted values are 555,090.65 million Rwandan francs in 2027 and, the ARIMA-ANN models forecasted values in 2027 are 552,881.33 million Rwandan francs. This study recommends the use of the ARIMA-ANN hybrid while modeling the health expenditure.
KeywordsARIMA; Box-Jenkins methodology; Artificial neural networks; Time series fore casting; Combined forecast
Published Date: 2023-04-17; Received Date: 2023-03-16
