The Use of Panel Data Models to Identify the Most Important Factors of Economic Growth in Arab Countries

Abstract

The study examined the use of panel data models in suggesting a statistical model to predict economic growth rates in Arab countries, and identifying the most important factors that contribute to increasing economic growth rates in the Arab countries, and measuring the relative importance of each of these factors, During the period from 2000 to 2019, The study sample consisted of 8 Arab countries: Qatar, Saudi Arabia, the Emirates, Jordan, Algeria, Morocco, Egypt and Sudan, It has been taken into account that the sample is for countries in which the growth rates of per capita GDP vary. Through the use of Hsiao test and tests of stationarity, it was confirmed that the panel data could be applied, and it was estimated through the use of three models: Pooled regression model, Fixed effects model, and Random effects model, Overall, the results of statistical tests indicates that the Fixed effects model is the best model, and The estimates of the proposed model parameters do not contradict the assumptions of economic theory and do not contradict practical reality, It was found that 56.89% of the changes that occur in economic growth in the Arab countries under study are due to: investment, trade openness, financial sector development, government spending, and the size of the labor force, It also turns out that the most important variables affecting economic growth in the Arab countries are the size of the labor force, followed by domestic and foreign direct investment, while the least variables affecting is trade openness, It was evident through the study that the use of panel data models increases the accuracy of statistical prediction because it takes into account the information with the time dimension in the time series, as well as the sectional dimension in the different units.