Prediction of the National Consumer Price Index in Mexico
Abstract
Consumer prices constitute the core indicator used to measure changes in the cost of a representative basket of goods and services, weighted according to their economic relevance, and serve as the main reference for estimating inflation. The objective of this study was to forecast the Mexican National Consumer Price Index (INPC) using two time series approaches: unobserved components models (UCM) and the seasonal autoregressive integrated moving average model (SARIMA), in order to compare their predictive performance. A quantitative descriptive and explanatory methodology was employed, using 288 monthly observations from january 2002 to december 2025. Both models were fitted using the SAS® statistical software, and their performance was evaluated based on out-of-sample Root Mean Squared Error (RMSE) for the period january 2023 to december 2025, as well as the Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC). In the results, the SARIMA (2,1,0)(0,1,1)[12] model showed a lower RECM, statistically significant coefficients and lower AIC and SBC values, surpassing the UCM model. The analysis is limited to a specific time series of the Mexican INPC; therefore, caution should be exercised when extending the results to other contexts. The sensitivity of UCM to structural changes suggests potential advantages in scenarios with elevated volatility. In conclusion, the SARIMA model demonstrated better predictive capacity and goodness of fit compared to the UCM model, suggesting that it is an effective tool for prospective analysis of inflationary behavior in Mexico and for decision-making in public policies.
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References
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