we propose a Bayesian Logistic Smooth Transition Autoregressive (LSTAR) model with stochastic volatility (SV) to model inflation dynamics in a nonlinear fashion. Inflationary regimes are determined by smoothed money growth which serves as a transition variable that governs the transition between regimes. We apply this approach to quarterly data from the US, the UK and Canada and are able to identify well-known, high inflation periods in the samples. Moreover, our results suggest that the role of money growth is specific to the economy under scrutiny and it can help to improve forecasting accuracy. Finally, we analyze a variety of different model specifications and are able to confirm that adjusted money growth still has leading indicator properties on inflation regimes.