Forecasting Inflation in Tunisia into instability: Using Dynamic Factors Model a two-step based on Kalman filtering

This work presents a forecasting inflation model using a monthly database. Conventional models for forecasting inflation use a small number of macroeconomic variables. In the context of globalization and dependent economic world, models have to account a large number of information. This model is the goal of recent research in the various industrialized countries as well as developing countries. With Dynamic Factors Model the forecast values are closer to actual inflation than those obtained from conventional models in the short term. In our research we devise the inflation in to “free inflation and administered inflation” and we test the performance of the DFM into instability (before and after revolution) in different types of inflation and trend inflation namely administered and free inflation. We found that dynamic factors model with factor instability leads to substantial forecasting improvements over dynamic factor model without instability factor in period after revolution.