Time Series Analysis: A Model Solution for Addressing Nigeria's Economic Challenges
Year: 2024
Type: article
Abstract: This study undertakes a comprehensive time series analysis of Nigeria's economic data from 2010 to 2023, focusing on key indicators such as GDP, inflation rate, unemployment rate, exchange rate, foreign direct investment (FDI), government revenue, and expenditure. Utilizing the ARIMA (Autoregressive Integrated Moving Average) model, we aim to forecast future economic trends and provide actionable insights for policy-making. Our analysis reveals significant volatility in Nigeria's economic indicators over the studied period. The GDP showed fluctuations, peaking in 2014 before experiencing a decline due to the 2016 recession, followed by gradual recovery. The inflation rate remained high, peaking at 19.5% in 2023, reflecting persistent inflationary pressures. Unemployment showed a troubling upward trend, reaching a height of 35.0% in 2021, indicating structural economic issues. The exchange rate exhibited substantial depreciation, while FDI inflows showed a declining trend, highlighting challenges in attracting foreign investment. Correlation analysis revealed critical relationships between these indicators: higher inflation rates and unemployment are associated with lower GDP growth, while higher exchange rates (weaker currency) associate with higher inflation. The ARIMA model forecast for GDP suggests continued recovery, but with significant uncertainties. The findings underscore the need for diversified economic strategies, effective inflation control measures, targeted job creation policies, and improved conditions for FDI. These insights are crucial for policymakers aiming to stabilize and grow Nigeria's economy. This study highlights the importance of data-driven approaches in addressing economic challenges and promoting sustainable growth in Nigeria.
Authors: Alfa, Mohammed Salisu, Stephen Dauda
Institution: Federal Polytechnic, Bida
Language: English
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4404882553