Time Series Analysis for Economic Forecasting Using ARIMA and GARCH Models

The growing complexity of global economies has intensified the need for accurate and reliable forecasting models to guide policy formulation, investment strategies, and risk management practices. This paper explores the application of two widely used econometric models Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) for economic forecasting. ARIMA models are particularly effective in capturing linear trends, autocorrelations, and seasonal dynamics within macroeconomic time series such as GDP growth and inflation rates, whereas GARCH models are specifically designed to handle time-varying volatility and volatility clustering, which are common in financial data such as stock market returns and exchange rates. The study emphasizes both theoretical underpinnings and practical implementation, detailing the process of model identification, parameter estimation, and diagnostic testing. Using real-world datasets, we evaluate forecasting accuracy through statistical measures including AIC, BIC, RMSE, and volatility persistence indicators. The comparative analysis demonstrates that ARIMA models excel in stable macroeconomic forecasting contexts, while GARCH models outperform in capturing risk dynamics and uncertainty in high-frequency financial series. Together, these findings highlight the complementary roles of ARIMA and GARCH, suggesting that integrated or hybrid approaches can offer superior predictive power by modeling both mean structures and conditional variances simultaneously. The paper contributes to the literature by providing empirical evidence and practical guidance on the use of ARIMA and GARCH in different economic contexts, underscoring their relevance for policymakers, financial analysts, and researchers seeking robust tools for economic decision-making.