AUTOREGRESSIVE DISTRIBUTED LAG MODELING FOR RICE PRICE PREDICTOR ANALYSIS IN BOJONEGORO REGENCY - Khoirina, Jami’atul; Nurdiansyah, Denny; Kartini, Alif Yuanita (Jurnal Statistika dan Aplikasinya, 2025)
Rice price fluctuations in Bojonegoro Regency are driven by complex interactions of economic, social, and environmental elements. These dynamics have a direct impact on the welfare of low-income households, making it essential to understand the underlying factors to support effective price stabilization efforts. Addressing this issue requires a comprehensive econometric model capable of capturing both immediate and lagged effects of relevant variables. This study analyzes the main drivers of rice price changes in Bojonegoro Regency by applying the Autoregressive Distributed Lag (ARDL) model. It focuses on how variables such as dried corn prices, rice consumption, harvest area, rice production, and money exchange rates contribute to rice price volatility. The ARDL model is employed to explore both short-term and long-term relationships between selected variables and rice prices. Model selection is guided by performance indicators including the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), R-Square, as well as results from stationarity, cointegration, and classical assumption tests. The study utilizes secondary data sourced from the Bojonegoro Regency Food Security and Agriculture Office and the Bojonegoro Statistics Agency. The optimal model, identified as ARDL (3,4,4,4,4,0), produces an R-Square of 97.13% and the lowest AIC among alternatives. The analysis reveals that dried corn prices, rice consumption, harvest area, and rice production significantly influence rice prices, each with distinct lag structures. The money exchange rate, however, is found to have no significant effect. This study does not account for policy-specific variables or broader external factors such as global climate change or international trade regulations, which may also impact rice prices. Additionally, the availability and quality of secondary data may affect the model’s predictive accuracy. By incorporating lag structures and localized economic factors, this research offers a robust predictive framework tailored to Bojonegoro Regency. It provides practical insights for policymakers aiming to enhance rice price stability and protect household purchasing power.