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Forecasting Inflation in a Macroeconomic Framework: An Application to Tunisia

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  • Souhaïb Chamseddine Zardi

    (Central Bank of Tunisia)

Abstract

The aim of this paper is to evaluate the relative performance of different forecasts of inflation methods for the case of Tunisia. For that, we use a large number of econometric models to forecast short-run inflation. Specifically, we use univariate models as Random Walk, SARIMA, a Time Varying Parameter model and a suite of multivariate autoregressive models as Bayesian VAR and Dynamic Factor models. The forecasting results suggest that models which incorporate more economic information outperform the benchmark random walk for the first two quarters ahead. Furthermore, we combine our forecasts by means and the results reveal that the combination of forecasts leads to a reduction in forecast errors compared to individual models.

Suggested Citation

  • Souhaïb Chamseddine Zardi, 2017. "Forecasting Inflation in a Macroeconomic Framework: An Application to Tunisia," IHEID Working Papers 07-2017, Economics Section, The Graduate Institute of International Studies.
    • Handle: RePEc:gii:giihei:heidwp07-2017
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    References listed on IDEAS

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    Keywords

    Short-run forecasting; Dynamic Factor Models; Forecast combination;
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