Short-Term Inflation Forecasting In Sierra Leone: A Comparison of Vector Autoregressive VAR(P), Arimax, And Arima Models
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This study develops and compares three time series models, ARIMA, ARIMAX, and VAR(p) for short-term inflation forecasting in Sierra Leone to aid evidence-based monetary policy formulation. Using monthly data from January 2018 to June 2023 on the Consumer Price Index (CPI), exchange rate (EXR), and money supply (M2), the study first confirmed that all series are integrated of order one (I(1)) using Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The ARIMA and ARIMAX models were estimated using Box-Jenkins methodology, with the ARIMAX model incorporating the exchange rate as an exogenous variable. A VAR(p) model was also specified using differenced series after cointegration tests showed no long-run relationship. Model performance was evaluated over a 12-month out-of-sample period (July 2023–June 2024) using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Results show that the ARIMAX model significantly outperformed ARIMA and VAR(p), achieving the lowest forecast errors (MAPE = 2.03%, RMSE = 4.76), reflecting a 71.8% and 83.3% improvement in MAPE over ARIMA and VAR(p), respectively. These findings confirm the exchange rate as a critical driver of short-term inflation in Sierra Leone. The superior performance of the ARIMAX model underscores the importance of including exogenous (exchange rate) information in inflation forecasting frameworks. Policymakers are advised to closely monitor exchange rate movements as a leading indicator of inflation, highlighting the centrality of exchange rate pass-through effects in inflation dynamics and to consider exogenous-variable-enriched models like ARIMAX for effective short-term inflation targeting.
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