Forecasting inflation in Tunisia during instability using dynamic factors model: a two-step based procedure based on Kalman filter

Bilel Ammouri, Hassen Toumi, Fakhri Issaoui and Habib Zitouna

International Journal of Computational Economics and Econometrics, 2019, vol. 9, issue 1/2, 49-83

Abstract: This work presents a forecasting inflation model using a monthly database. The model has to take into account a large amount of information, is the goal of recent research in various industrialised countries as well as developing ones. With the dynamic factors model (DFM), the forecast values are closer to the actual inflation than those obtained from the conventional models in the short term. In our research, we devise the inflation into 'free and administered' and test the performance of the DFM under instability in different types of inflation (core and trend). Knowing that periods of instability are simultaneously the period of price liberalisation of basic goods (2008) and the post-revolution (the Arabic spring) period (2011-2014). We have found that the DFM with an instability factor leads to substantial forecasting improvements over the DFM without an instability factor in the period after the revolution.

Keywords: inflation forecasting; PCA; principal component analysis; VAR; vector autoregressive; DFM; dynamic factors model; Kalman filter; space-state; instability factor. (search for similar items in EconPapers)
Date: 2019
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