Peramalan Tingkat Inflasi di Indonesia Menggunakan Pendekatan Bayesian Vector Autoregressive (BVAR) - Widyanti Rahayu; Farah Dhiya Fadilah; Vera Maya Santi (Limits: Journal of Mathematics and Its Applications, 2026)
Inflation is a key indicator of a country’s economic stability, influenced by both domestic and global factors, making accurate forecasting essential for effective policy decisions. This study forecasts Indonesia’s inflation rate using the Bayesian Vector Autoregressive (BVAR) approach and compares its performance with the classical Vector Autoregressive (VAR) model. The variables analyzed include the inflation rate, BI Rate, Consumer Price Index (CPI), and USD exchange rate (KURS), with monthly data from January 2010 to December 2024 obtained from Bank Indonesia (BI) and Central Bureau Statistics (BPS). The dataset was divided into 90% training data for model estimation and 10% testing data for accuracy evaluation. VAR parameters were estimated using Maximum Likelihood Estimation (MLE), while BVAR parameters employed the Minnesota prior, enabling analytical posterior derivation. The research findings are expected to contribute to the achievement of the Sustainable Development Goals (SDGs), particularly SDG 8. Forecasting performance was assessed using predictive accuracy metrics. The 18-month forecasts indicate that BVAR outperforms VAR, with Mean Absolute Percentage Error (MAPE) values of 12.43% and 52.18%, respectively. Impulse Response Function (IRF) analysis reveals that inflation responds significantly to short-term shocks in the exchange rate and CPI. Forecast Error Variance Decomposition (FEVD) shows that inflation variability is primarily driven by its own shocks, followed by exchange rate and interest rate fluctuations. These findings demonstrate that the BVAR model with Minnesota prior provides more accurate and stable inflation forecasts than the classical VAR approach, offering valuable insights for monetary policy formulation in Indonesia.