Forecasting Mexican Inflation using Neural Networks

In this work we use a Neural Network model to forecast Mexican inflation. Related works forecast inflation in countries where this economic variable has a stable behavior while Mexican inflation has been characterized to be very volatile during certain periods. There were implemented different Neural Network models by varying the number of hidden layers (1 and 2) and the number of neurons in the hidden layer (from 1 to 100). The forecasting model results were divided into 3 categories: a volatile inflation phase, where the mean difference was 0.64% between real inflation and forecasted inflation; a transition phase, where the mean difference was 5.44%; and a stability phase, where the mean difference was 0.28%. By doing a comparison between model forecasting results and Bank of Mexico's predictions, Neural Networks model results are clearly more accurate to the real inflation behavior, a critical point during inflationary crisis periods.