MULTIVARIATE TIME SERIES MODELING USING VECTOR AUTOREGRESSION FOR RICE PRICE PREDICTION IN INDONESIA

Atika Ratna Dewi (Data Science Study Program, Telkom University, Purwokerto Campus, Indonesia)
Andreas Rony Wijaya (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia)
Mirza Ghanimi (Data Science Study Program, Telkom University, Purwokerto Campus, Indonesia)
Talitha Veda Azaria Ramadhani (Data Science Study Program, Telkom University, Purwokerto Campus, Indonesia)

BAREKENG: Jurnal Ilmu Matematika dan Terapan
Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application

This study analyzes the dynamic relationship between rice prices and selected economic variables using a Vector Autoregression (VAR) model. The analysis utilizes daily data from January 2022 to December 2023, encompassing rice prices, chicken meat prices, chicken egg prices, the Rupiah-to-USD exchange rate, inflation, and crude oil prices. The estimated VAR model is stable, as all eigenvalues lie within the unit circle. Residual diagnostics based on the Portmanteau (Ljung–Box) test indicate no residual autocorrelation across all equations (LB statistics with df = 1, p-values > 0.05), confirming the adequacy of the model specification. The model demonstrates good predictive performance for the rice-price series, achieving a Mean Absolute Percentage Error (MAPE) of 0.42% over the out-of-sample testing period (the last 20% of observations). Empirical results suggest that rice prices are influenced by dynamic interactions within the system, particularly through their relationships with chicken meat prices and the Rupiah–USD exchange rate. These findings offer valuable policy insights for maintaining rice price stability, a crucial component of national food security.