In this study, I forecast CPI inflation in Russia by the method of Dynamic Model Averaging (Raftery et al., 2010; Koop and Korobilis, 2012) pseudo out-of-sample on historical data. This method can be viewed as an extension of the Bayesian Model Averaging where the identity of a model that generates data and model parameters are allowed to change over time. The DMA is shown not to produce forecasts superior to simpler benchmarks even if a subset of individual predictors is pre-selected “with the benefit of hindsight” on the full sample. The two groups of predictors that feature the highest average values of the posterior inclusion probability are loans to non-financial firms and individuals along with actual and anticipated wages.