| By: |
Cesar Carrera (Banco Central de Reserva del Perú);
Alan Ledesma (UC Santa Cruz) |
| Abstract: |
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. |
| Keywords: |
Inflation forecasting, aggregate forecast, Bayesian VAR |
| JEL: |
C22 C52 C53 E37 |
| Date: |
2015–07 |
| URL: |
http://d.repec.org/n?u=RePEc:apc:wpaper:2015-050&r=ets |