A Statistical Study to Predict to Development Index of Information and Communication Technology ( ICT ) in the Arab Countries
Aboul Ela; Mowafy , Mamdouh;
Abstract
This study aims to predict to development index of information and communication technology in the Arab Countries and appreciation of the most important factors affecting it and to achieve this goal is the use of Panel Data method , which combines time-series data, and with the cross-section data, this method is based on three basic models, a pooled regression model (PRM), fixed effects model (FEM), and random effects model (REM) , and to determine which of these models best to predict development index of information and communication technology and measurement of the factors affecting the test was used Wald Test for comparison between the two models FEM and PRM and test Hausman to choose between the two models FEM and REM, the variables of the study is the ICT Development Index (IDI) as the dependent variable, and the independent variables which represent in the number of subscribers of fixed and mobile phones, the number of Internet subscribers, the number of the population, the proportion of imports information and communication technologies, The proportion of ICT exports, the rate of literacy, and the rate of gross enrollment for higher education, the results of study show that the fixed effects model is the best model and it has the highest explanatory and predictive ability, and found through appreciation significant variables which include number of subscribers of fixed and mobile phones, the number of Internet subscribers, The proportion of imports of information and communication technology, and the proportion of ICT exports, and other variables are not significant.
Other data
Title | A Statistical Study to Predict to Development Index of Information and Communication Technology ( ICT ) in the Arab Countries | Authors | Aboul Ela ; Mowafy , Mamdouh | Keywords | Information and Communication Technology (ICT) , ICT Development Index ( IDI ) , Pooled Regression Model (PRM) , Fixed Effects Model ( FEM ) , Random Effects Model ( REM ). | Issue Date | Sep-2016 | Publisher | Pushpa Publishing House, Allahabad, India | Journal | Advances and Applications in Statistics |
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