Macroeconomic Drivers of Inflation in Mexico: Insights from Machine Learning Predictions

Abstract

This study investigates the key macroeconomic drivers of inflation in Mexico using a machine learning approach, focusing on the interplay between exchange rates, energy prices, and monetary policy. By employing a neural network model, the research captures complex, nonlinear relationships among macroeconomic variables, offering enhanced predictive accuracy compared to traditional models. Findings reveal that exchange rate volatility and energy price fluctuations are the most significant contributors to inflationary pressures, particularly during major economic shocks such as the 2016 peso depreciation and the 2017 energy market deregulation. The study provides actionable insights for policymakers, emphasizing the importance of stabilizing exchange rates and managing energy costs to mitigate inflation risks. These results demonstrate the value of integrating machine learning tools into economic analysis for informed decision-making.