DT 2015-03: Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos
DT 2015-03: Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos
DT 2015-03: Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos
N°
DT 2015-03
Título
Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos
Título original
Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models
Autores
Cesar Carrera y Alan Ledesma
Lenguaje
Inglés
Fecha
2015/07/01
Resumen
We forecast 18 groups of individual components of the Consumer Price Index (CPI) using a large Bayesian vector autoregressive
model (BVAR) and then aggregate those forecasts in order to obtain a headline inflation forecast (bottom-up approach). De Mol et al. (2006)
and Banbura et al. (2010) show that BVAR's forecasts can be significantly improved by the appropriate selection of the shrinkage
hyperparameter. We follow Banbura et al. (2010)'s strategy of "mixed priors," estimate the shrinkage parameter, and forecast inflation. Our
findings suggest that this strategy for modeling outperform the benchmark random walk as well as other strategies for forecasting
inflation.