@article{Zardi:294973,
      recid = {294973},
      author = {Zardi, Souhaïb Chamseddine},
      title = {Forecasting inflation in a macroeconomic framework an  application to Tunisia},
      publisher = {The Graduate Institute of International and Development  Studies, International Economics Department},
      address = {Geneva. 2017},
      number = {BOOK},
      series = {Graduate Institute of International and Development  Studies Working Paper ; no. 07/2017},
      pages = {24 p. : ill.},
      year = {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.},
      url = {http://repository.graduateinstitute.ch/record/294973},
      doi = {https://doi.org/10.71609/iheid-77zd-vn31},
}