EVALUATING TIME SERIES PREDICTION MODELS: EGG PRICES IN MEXICO

Abstract

Mexico is the world’s largest consumer of eggs, producing 3.05 million Mg in 2021. The high variation in wholesale prices is a feature of the egg production system, which is important to producers and government institutions that need to forecast future prices for activity planning. As a result, it is necessary to propose tools that can reliably predict egg prices. The goal of this paper was to compare the performance of various statistical models by analyzing the time series of egg prices using the Akaike index and forecast error to determine which model best predicts the wholesale price of white eggs. The models evaluated were the autoregressive integrated moving average model (ARIMA), ARIMA with interventions, ARIMA with transfers, and regression with ARIMA errors. Two time series were used: the wholesale price of white eggs, constructed with data from the National System of Information and Market Integration (SNIIM) and the Agrifood and Fisheries Information Service (SIAP), and egg imports, calculated with data from the Economic Information System. The latter was used as an exogenous variable to explain the price of eggs. Both cover the period from January 2006 to December 2021. According to the Akaike index, the model with the best adjustment was ARIMA (0,1,1)(1,0,1)[12] with interventions. In the evaluation of forecast error, the best models were the regression models with ARIMA (1,1,0)(1,0,1)[12] and ARIMA (1,1,0)(1,0,1)[12] errors with transfer.