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Forecasting Inflation in Argentina

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  • Garegnani, Lorena
  • Gómez Aguirre, Maximiliano

Abstract

In 2016 the Central Bank of Argentina began to announce inflation targets. In this context, providing authorities with good estimates of relevant macroeconomic variables is crucial for making pertinent corrections in order 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 longer forecast horizons.

Suggested Citation

  • Garegnani, Lorena & Gómez Aguirre, Maximiliano, 2018. "Forecasting Inflation in Argentina," IDB Publications (Working Papers) 8940, Inter-American Development Bank.
    • Handle: RePEc:idb:brikps:8940
    DOI: http://dx.doi.org/10.18235/0001160
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian Vector Autoregressive; Forecasting; Prior specification; Marginal likelihood; Small-scale and large-scale models;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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