Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models
Cesar Carrera and
Alan Ledesma Arista
No 50, Working Papers from Peruvian Economic Association
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 (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ets, nep-for, nep-mac, nep-mon and nep-ore
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