Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912063039733760 |
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| author | Baidoo, Theophilus G. Obeng, Ashley |
| author_facet | Baidoo, Theophilus G. Obeng, Ashley |
| contents | Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05630 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies Baidoo, Theophilus G. Obeng, Ashley Econometrics Machine Learning Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management. |
| title | Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2410.05630 |