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Predicción de la inflación en México con modelos desagregados por componente

Author

Listed:
  • Robinson Durán

    (Universidad de Concepción)

  • Evelyn Garrido

    (Universidad de Concepción)

  • Carolina Godoy

    (Banco Central de Chile)

  • Juan de Dios Tena

    (Università di Sassari y Universidad Carlos III)

Abstract

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.

Suggested Citation

  • Robinson Durán & Evelyn Garrido & Carolina Godoy & Juan de Dios Tena, 2012. "Predicción de la inflación en México con modelos desagregados por componente," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 133-167.
    • Handle: RePEc:emx:esteco:v:27:y:2012:i:1:p:133-167
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    File URL: https://estudioseconomicos.colmex.mx/index.php/economicos/article/view/93/95
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    References listed on IDEAS

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    More about this item

    Keywords

    forecasting Mexican inflation; vector error correction models; fixed effect models; dynamic factors;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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