The association between health expenditure, institutions, and economic growth in MENA countries.

Background: Expenditure on health is vital in the development of a country. Furthermore, the current COVID-19 pandemic emphasises the importance of health investments in maintaining a healthier economy worldwide. A substantial amount of empirical research on the relationship between health expenditure and economic growth yields conflicting results. The study intends to investigate the relationship between health spending and economic growth and institutions' role in causing health spending to promote growth. Methods: The study uses longitudinal data to examine the relationship between health spending and economic growth in seven MENA countries from 2000 to 2017. The study uses the Phillips Perron (PP) Fisher chi-square stationarity test, indicating that the data series is not stationary. Following this, we used the Pedroni test for cointegration, and the results show long-run relationships between the variables. Next, Granger causality determines the direction of causality. Finally, panel data methods of panel ordinary least squares (Panel OLS), fully modified OLS (FMOLS), and dynamic OLS (DLOS) supplement the findings. Results: The Pedroni cointegration test (P value<0.0001) indicates that the variables have a long-run cointegrating relationship. On the other hand, the Granger causality test finds no causal relationships between health spending and economic growth. Furthermore, the panel data models show that expenditure on health does not directly contribute to higher economic growth in MENA countries. Conclusion: The findings of this study indicate that health spending does not lead to increased economic growth; this could be due to poor institutional quality. However, for health spending to positively impact economic growth, these investments in health care must be supplemented by other factors, particularly institutions.

Predicting Crude Oil Prices During a Pandemic: A Comparison of Arima and Garch Models

The unprecedented global turn of events primarily due to the spread of highly contagious corona pandemic has led to a substantial fall in crude oil prices. A forecast for crude oil prices is important as oil is required for all major economic activity, particularly production and transportation. This study aims to apply two commonly used methods of Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) to predict the WTI crude oil prices for the period February 10, 2020, to April 27, 2020. Such a comparative analysis of these methods in unprecedented times is missing in the existing literature. ARIMA suggests ARIMA (4,1,4) model while GARCH (1,1) as the best among their own respective family of models. And between ARIMA and GARCH ARIMA model is recommended for forecasting as it has a lower root mean squared error (RMSE) and mean absolute error (MAE). The study recommends using a mean based ARIMA approach for predicting future values in extreme situations.