Inflation prediction in Mexico with models disaggregated by components

  • Robinson Duran Universidad de Concepción
  • Evelyn Garrido Universidad de Concepción
  • Carolina Godoy Banco Central de Chile
  • Juan de Dios Tena Università di Sassari and Universidad Carlos III
Keywords: forecasting Mexican inflation, vector error correction models, fixed effect models, dynamic factors
JEL Classification: C2, C3, C5


This article is an empirical analysis on the optimal level of disaggregation by sectors and the best econometric strategy in order to forecast Mexican inflation. We compare different disaggregate modeling strategies based on: 1) univariate ARIMA models, 2) panel data methodology, 3) vector error correction models, and 4) dynamic common factor models. It is found that disaggregation by sectors is useful in order to forecast the Mexican inflation rate. Moreover, inflation forecasts based on panel data, vector correction models and dynamic factor models improves those obtained from simple extrapolative devices based on ARIMA models.


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How to Cite
DuranR., GarridoE., GodoyC., & de Dios TenaJ. (2012). Inflation prediction in Mexico with models disaggregated by components. Estudios Económicos, 27(1), 133-167.
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