(untitled) (Marketing Journal, 2025)
This study develops an econometric forecasting model for the prices of key food commodities — beef, potatoes, and rice. Using five years of historical price data provided by the Statistics Agency, price forecasts for the next six months were generated using ARIMA (Autoregressive Integrated Moving Average) models. The relevance of this research stems from the high volatility in the prices of these goods, which directly affects living standards, inflation rates, economic stability, and social development. The developed model can be used by government institutions to predict price dynamics, regulate the market in a timely manner, and prevent crisis scenarios. The methodology includes analyzing five years of historical data, testing time series stationarity (ADF test), using ACF/PACF plots and AIC/BIC criteria to select optimal ARIMA (r, d, q) parameters, and evaluating forecast accuracy using RMSE and MAE metri cs. Results show that in the absence of external factors, the model performs effectively for beef, potatoes, and rice (RMSE = 2.1). The study demonstrates the effectiveness of the ARIMA model in addressing food security issues and highlights the importance of incorporating exogenous factors in future research.