Modeling Inflation, Exchange Rate, and Interest Rate of Sri Lanka: Time Series Approach withPolitical, Natural, and Health Crises

In time series analysis, external shocks are essential for accurately capturing the effects of sudden, non-recurring events, enabling more precise estimation and forecasting. This study examines Sri Lankan economic variables including monthly year-on-year inflation rates, exchange rates, and interest rates using monthly data from 2003 to 2023 obtained from the International Monetary Fund (IMF) database and the Central Bank of Sri Lanka. Dummy variables for natural disasters, health crises, and political events were included to account for external shocks. Time series plots of inflation, exchange rates, and interest rates were generated to identify trends, seasonal patterns, and anomalies. Cointegration of the residuals of the fitted models was tested, revealing the presence of cointegration. Three Vector Error Correction (VEC) models were estimated, with one model selected based on information criteria and model diagnostic techniques. Model diagnostics ensured the reliability of the VEC models, and the VEC model with log transformed time series variables with dummy variables was chosen due to its lowest Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Diagnostic tests, including the Portmanteau test for serial correlation and stability checks, were conducted. The Portmanteau test revealed significant autocorrelation in the residuals, indicating that the VEC model may not fully capture the data’s dynamics and might need modifications. However, stability analysis confirmed that all eigenvalues of the selected VEC model are within the unit circle, ensuring reliable forecasts. The selected VEC model provides valuable insights into Sri Lanka’s economic variables. Nonetheless, the findings suggest that further adjustments may be needed to address residual autocorrelation.