In the rapidly evolving economic landscape of Ghana, understanding the intricate interdependencies between macroeconomic variables is pivotal for informed policymaking and strategic economic planning. The study employed network analysis to enhance our comprehension of Ghana's macroeconomic dynamics. Data was sourced from the world development indicators. Initially, a statistical network was constructed to represent the interconnections between Ghana's principal macroeconomic variables using partial correlation matrix, offering a visual and analytical perspective of their relationships. Subsequently, centrality measures and other network analysis tools were utilized to identify and quantify the influence of key economic indicators within this network. Results showed that Exports, Inflation, Exchange rate, Gross Domestic Saving, Manufacturing and Gross National Expenditure played a significant role in the network. However, Agriculture and Imports were identified as most influential variables with high centrality scores across all centrality measures. Finally, Exponential Random Graph Model was employed to provide a comparative baseline, shedding light on the uniqueness or randomness of the observed interrelationships. The significant parameters in the model include the presence of edges between nodes and the presence of generalized geodesic triads (gwesp), which capture the tendency for nodes to form connections based on common neighbors. The findings also revealed that there is a probability of 16.19% for a relationship to exist between two macroeconomic variables if they are both connected to the same third variable.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 13, Issue 6) |
| DOI | 10.11648/j.ajtas.20241306.15 |
| Page(s) | 227-241 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Macroeconomic Variables, Statistical Network, Partial Correlation, Exponential Random Graph Model
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APA Style
Atindana, E., Engmann, G. M., Azaare, J. (2024). Statistical Network Analysis of Macroeconomic Variables in Ghana. American Journal of Theoretical and Applied Statistics, 13(6), 227-241. https://doi.org/10.11648/j.ajtas.20241306.15
ACS Style
Atindana, E.; Engmann, G. M.; Azaare, J. Statistical Network Analysis of Macroeconomic Variables in Ghana. Am. J. Theor. Appl. Stat. 2024, 13(6), 227-241. doi: 10.11648/j.ajtas.20241306.15
AMA Style
Atindana E, Engmann GM, Azaare J. Statistical Network Analysis of Macroeconomic Variables in Ghana. Am J Theor Appl Stat. 2024;13(6):227-241. doi: 10.11648/j.ajtas.20241306.15
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Download@article{10.11648/j.ajtas.20241306.15,
author = {Elijah Atindana and Gideon Mensah Engmann and Jacob Azaare},
title = {Statistical Network Analysis of Macroeconomic Variables in Ghana
},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {13},
number = {6},
pages = {227-241},
doi = {10.11648/j.ajtas.20241306.15},
url = {https://doi.org/10.11648/j.ajtas.20241306.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20241306.15},
abstract = {In the rapidly evolving economic landscape of Ghana, understanding the intricate interdependencies between macroeconomic variables is pivotal for informed policymaking and strategic economic planning. The study employed network analysis to enhance our comprehension of Ghana's macroeconomic dynamics. Data was sourced from the world development indicators. Initially, a statistical network was constructed to represent the interconnections between Ghana's principal macroeconomic variables using partial correlation matrix, offering a visual and analytical perspective of their relationships. Subsequently, centrality measures and other network analysis tools were utilized to identify and quantify the influence of key economic indicators within this network. Results showed that Exports, Inflation, Exchange rate, Gross Domestic Saving, Manufacturing and Gross National Expenditure played a significant role in the network. However, Agriculture and Imports were identified as most influential variables with high centrality scores across all centrality measures. Finally, Exponential Random Graph Model was employed to provide a comparative baseline, shedding light on the uniqueness or randomness of the observed interrelationships. The significant parameters in the model include the presence of edges between nodes and the presence of generalized geodesic triads (gwesp), which capture the tendency for nodes to form connections based on common neighbors. The findings also revealed that there is a probability of 16.19% for a relationship to exist between two macroeconomic variables if they are both connected to the same third variable.
},
year = {2024}
}
TY - JOUR T1 - Statistical Network Analysis of Macroeconomic Variables in Ghana AU - Elijah Atindana AU - Gideon Mensah Engmann AU - Jacob Azaare Y1 - 2024/12/16 PY - 2024 N1 - https://doi.org/10.11648/j.ajtas.20241306.15 DO - 10.11648/j.ajtas.20241306.15 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 227 EP - 241 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20241306.15 AB - In the rapidly evolving economic landscape of Ghana, understanding the intricate interdependencies between macroeconomic variables is pivotal for informed policymaking and strategic economic planning. The study employed network analysis to enhance our comprehension of Ghana's macroeconomic dynamics. Data was sourced from the world development indicators. Initially, a statistical network was constructed to represent the interconnections between Ghana's principal macroeconomic variables using partial correlation matrix, offering a visual and analytical perspective of their relationships. Subsequently, centrality measures and other network analysis tools were utilized to identify and quantify the influence of key economic indicators within this network. Results showed that Exports, Inflation, Exchange rate, Gross Domestic Saving, Manufacturing and Gross National Expenditure played a significant role in the network. However, Agriculture and Imports were identified as most influential variables with high centrality scores across all centrality measures. Finally, Exponential Random Graph Model was employed to provide a comparative baseline, shedding light on the uniqueness or randomness of the observed interrelationships. The significant parameters in the model include the presence of edges between nodes and the presence of generalized geodesic triads (gwesp), which capture the tendency for nodes to form connections based on common neighbors. The findings also revealed that there is a probability of 16.19% for a relationship to exist between two macroeconomic variables if they are both connected to the same third variable. VL - 13 IS - 6 ER -
Department of Statistics and Actuarial Sciences, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
Department of Biometry, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
Department of Business Computing, School of Computing and Information Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
