Forecasting the Behavior of Peruvian Coffee Export Prices in International Markets Using Econometric Models, 2010–2025

High coffee price volatility increases uncertainty regarding harvest profitability and marketing price setting, affecting both coffee growers and producing and exporting firms. This study aims to analyze and forecast the unit export price of Peruvian coffee using monthly data for the 2010–2024 period, with projections for 2025, in order to provide an applied decision-support tool. To this end, six econometric and time-series specifications were compared: a linear model, a quadratic model, additive Holt–Winters exponential smoothing, a benchmark causal model, a lagged model, and an ARIMA model. Model performance was assessed using goodness-of-fit and diagnostic criteria, including the coefficient of determination (R²), the Akaike Information Criterion, and the Durbin–Watson statistic, complemented by forecast error metrics for the exponential smoothing model. The results show that the linear and quadratic models exhibit non-significant trend terms and residual autocorrelation. The Holt–Winters model reported a root mean squared error of 5.45, although it did not show a robust contribution of trend and seasonal components. Likewise, the models based on FOB value and net weight displayed low explanatory power and persistent autocorrelation. In contrast, the ARIMA(1,0,0) model showed the most consistent statistical performance, with the lowest AIC (6.220984) and a Durbin–Watson statistic close to 2.00, suggesting the absence of residual autocorrelation. Its forecasts indicate stabilization from March 2025 onward, with values converging around 8.15. Overall, the evidence supports ARIMA as an operational tool for price monitoring and export planning. As a future research agenda, this approach could be complemented with GARCH models to capture volatility and contrasted with nonlinear methods under out-of-sample validation schemes.