This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions ability to flexibly whole distribution inflation. In order make our approach accessible and relevant forecasting, we derive an efficient Gibbs sampler by transforming stat...

Abstract This paper investigates the use of dynamic model averaging (DMA) approach for identifying good inflation predictors and forecasting in Mongolia, one most commodity-dependent economies. The DMA allows both a set marginal effects to change over time. Our empirical work resulted several novel findings. First, external variables (i.e., China’s growth, inflation, oil price) play an importan...