Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies

• URL: http://arxiv.org/abs/2410.05630v1
• Date: Tue, 8 Oct 2024 02:26:50 GMT
• Title: Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies
• Authors: Theophilus G. Baidoo, Ashley Obeng,
• Abstract summary: This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana. utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models to predict future inflation trends.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Harnessing Generative AI for Economic Insights [1.3344743915135726]
  We use generative AI to extract managerial expectations about their economic outlook from over 120,000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment.
  arXiv  Detail & Related papers  (2024-10-04T19:57:37Z)
• Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010) [0.0]
  This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment.
  arXiv  Detail & Related papers  (2024-09-12T19:01:16Z)
• Machine Learning for Economic Forecasting: An Application to China's GDP Growth [2.899333881379661]
  This paper employs various machine learning models to predict the quarterly real GDP growth of China. It analyzes the factors contributing to the performance differences among these models.
  arXiv  Detail & Related papers  (2024-07-04T03:04:55Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Macroeconomic Predictions using Payments Data and Machine Learning [0.0]
  This paper aims to demonstrate that non-traditional and timely data can provide policymakers with sophisticated models to accurately estimate key macroeconomic indicators in near real-time. We provide a set of econometric tools to mitigate overfitting and interpretability challenges in machine learning models to improve their effectiveness for policy use. Our models with payments data, nonlinear methods, and tailored cross-validation approaches help improve macroeconomic nowcasting accuracy up to 40% -- with higher gains during the COVID-19 period.
  arXiv  Detail & Related papers  (2022-09-02T11:12:10Z)
• Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty [0.0]
  Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU) Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty.
  arXiv  Detail & Related papers  (2022-08-10T09:08:29Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning [126.37520136341094]
  We show that machine-learning-based economic simulation is a powerful policy and mechanism design framework. The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt. In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory.
  arXiv  Detail & Related papers  (2021-08-05T17:42:35Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.