Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/162468
Authors: 
Zardi, Souhaïb Chamseddine
Year of Publication: 
2017
Series/Report no.: 
Graduate Institute of International and Development Studies Working Paper 07-2017
Abstract: 
The aim of this paper is to demonstrate the relative performance of combining forecasts on inflation in 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. Results of forecasting 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 finding results reveal that the forecast combination leads to a reduction in forecast error compared to individual models.
Subjects: 
short-run forecasting
dynamic factor models
forecast combination
Document Type: 
Working Paper

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