Modelos FAVAR con factores estáticos y dinámicos para pronosticar la inflación en Costa Rica

This study presents a methodology for forecasting inflation in Costa Rica using a FAVAR model that combines data from 156 relevant time series. This approach consists of two stages: first, static and dynamic factors are estimated, which are then incorporated into a VAR model along with monthly inflation to project the annual variation of the Consumer Price Index. Automatic selection criteria are employed to choose which variables to include in the factors and to determine the number of factors, lags, and restrictions on the coefficients of the VAR model. Eight inflation forecasts are generated and combined using three averages: simple, inverse mean squared error weighted, and Bayesian. The results indicate that the Bayesian forecast is the most accurate for the period between 2021 and 2023, outperforming even the most accurate of traditional VAR models that consider only inflation and individually any of the 156 variables. This suggests that the FAVAR model can effectively integrate information from available variables without requiring prior knowledge of which ones are most relevant.