2019-05-292019-05-292014-11-20http://repositorio.redinvestigadores.org/handle/Riec/16Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power; all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002-2011 as prior information. Our results show that: 1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and 2) priors consisting of staff's estimates outperform all other priors that comprise equal or zero-vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon.20 páginas : gráficas, tablasPDFengOpen AccessWorking paperC22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion processesC53 - Forecasting and Prediction Methods; Simulation MethodsC11 - Bayesian Analysis: GeneralE31 - Price Level; Inflation; DeflationBayesian shrinkageInflation forecast combinationInternal forecastsRolling window estimationTasas de inflación -- Pronósticos -- ModelosBancos centrales -- ModelosInflación -- Intervención del estado -- Colombia -- 2002-2011Análisis bayesianoAcceso abiertoAtribucion-NoComercial-CompartirIgual CC BY-NC-SA 4.0C11 - Análisis bayesiano: generalidadesC22 - Modelos de series temporales; Regresiones cuantiles dinámicas; Modelos dinámicos de tratamiento; procesos de difusiónC53 - Métodos de pronóstico y predicción; métodos de simulaciónE31 - Nivel de precios; Inflación; Deflación