Principal Component Analysis for the Egyptian Economic Growth Under the Government’s Vision 2030
Ezzat, H. (2021). Principal Component Analysis for the Egyptian Economic Growth Under the Government’s Vision 2030. EKB Journal Management System, 3(2), 110-129. doi: 10.21608/ijimct.2021.49854.1008
Heba Ezzat. "Principal Component Analysis for the Egyptian Economic Growth Under the Government’s Vision 2030". EKB Journal Management System, 3, 2, 2021, 110-129. doi: 10.21608/ijimct.2021.49854.1008
Ezzat, H. (2021). 'Principal Component Analysis for the Egyptian Economic Growth Under the Government’s Vision 2030', EKB Journal Management System, 3(2), pp. 110-129. doi: 10.21608/ijimct.2021.49854.1008
Ezzat, H. Principal Component Analysis for the Egyptian Economic Growth Under the Government’s Vision 2030. EKB Journal Management System, 2021; 3(2): 110-129. doi: 10.21608/ijimct.2021.49854.1008

Principal Component Analysis for the Egyptian Economic Growth Under the Government’s Vision 2030

The International Journal of Informatics, Media and Communication Technology
Article 4, Volume 3, Issue 2, December 2021, Page 110-129  XML PDF (1.27 MB)
Document Type: Original Article
DOI: 10.21608/ijimct.2021.49854.1008
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Author
Heba Ezzat email orcid
Department of Socio-Computing, Faculty of Economics and Political Science, Cairo University, Cairo, Egypt
Abstract
Under the Vision 2030, Egyptian economy is planned to be balanced, knowledge-based, competitive, and diversified. A knowledge-based economy depends on knowledge, information, and high skills that substantially contribute to economic growth and innovation in services in advanced economies. Economies characterized by knowledge-based activities support a significant share of GDP growth. Thereby, identifying components of knowledge-based economy that could help approaching the Vision 2030 is very important. In this paper, six pillars for knowledge-based economy were selected: education, innovation, information and communication technology, development, employment, and human capital. As each pillar contains many indicators, we follow the Principal Component Analysis (PCA) to reduce predictors’ dimensions. Results of the PCA show that, out of 35 variables, only 22 are highly affecting the GDP. The first principal component explains about 88% of the variance. Principal Component Regression (PCR) is built to predict the effect of these indicators on the GDP. The predictive performance of the PCR model is assessed following the cross-validation technique. Results reveal that, the minimal Mean Squared Error of Prediction (MSEP) could be reached at the first PC. Additionally, the PCR model explains most of the variability of the response data around its mean (R-squared estimated as 0.99).
Keywords
Education; innovation; employment; information and communication technology; principal component analysis
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