Forecasting Indonesia’s Inflation-at-Risk Using Vector Autoregression with Exogenous Variables Model
DOI:
https://doi.org/10.22452/mjs.vol45sp2.10Keywords:
IaR, FCI, FSI, VARX, ForecastingAbstract
Inflation is a critical economic indicator influencing stability and monetary policy, making accurate forecasting essential. This study employs the Vector Autoregression with Exogenous Variables (VARX) model to simultaneously forecast Indonesia's inflation across various sectors, including food, health, housing, clothing, and education, incorporating the Financial Condition Index (FCI) and Financial Stress Index (FSI). The FSI evaluates financial stress using data from banking, foreign exchange, stock markets, and government securities, while the FCI assesses economic conditions through Principal Component Analysis (PCA). VARX is rarely used in this context and differs from previous studies in that it focuses on the risk of extreme inflation. In addition, this study introduces the use of the Correlation Information Criterion (CIC) as an evaluation tool, which has not been widely applied in similar research. The fluctuation patterns of each sectoral inflation generally display distinct characteristics, enabling a comprehensive evaluation of the VARX model’s ability to forecast data with varying fluctuation patterns. This approach differs from previous studies, which typically utilize data with uniform fluctuation patterns. Furthermore, the Seasonal Trend Decomposition Based on Loess (STL) method is employed to mitigate seasonal effects in the data and improve forecasting accuracy. This technique is rarely applied in this context. Analyzing monthly data from February 2010 to December 2024, the VARX(1) model effectively forecasts Indonesia's Inflation-at-Risk (IaR) with high accuracy, as measured using the Symmetric Mean Absolute Percentage Error (SMAPE) criterion. Seasonal inflation in the health sector is addressed using the Seasonal Trend Decomposition Based on Loess (STL) method, which further improves forecasting precision. However, the model struggles with extreme fluctuations, highlighting the need for future research into more advanced predictive models for volatile economic conditions.
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