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Forecasting Inflation in Argentina

Author

Listed:
  • Lorena Garegnani

    (Central Bank of Argentina)

  • Mauricio Gómez Aguirre

    (Central Bank of Argentina)

Abstract

During the year 2016, the Central Bank of Argentina has begun to announce inflation targets. In this context, providing the authorities of good estimates of relevant macroeconomic variables turns out to be crucial to make the pertinent corrections to reach the desired policy goals. This paper develops a group of models to forecast inflation for Argentina, which includes autoregressive models, and different scale Bayesian VARs (BVAR), and compares their relative accuracy. The results show that the BVAR model can improve the forecast ability of the univariate autoregressive benchmark’s model of inflation. The Giacomini-White test indicates that a BVAR performs better than the benchmark in all forecast horizons. Statistical differences between the two BVAR model specifications (small and large-scale) are not found. However, looking at the RMSEs, one can see that the larger model seems to perform better for larger forecast horizons.

Suggested Citation

  • Lorena Garegnani & Mauricio Gómez Aguirre, 2018. "Forecasting Inflation in Argentina," BCRA Working Paper Series 201879, Central Bank of Argentina, Economic Research Department.
    • Handle: RePEc:bcr:wpaper:201879
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    More about this item

    Keywords

    Bayesian vector autoregression; forecasting; prior specification; marginal likelihood; small-scale and large-scale models;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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