Abstract The present paper compares expected inflation to (econometric) inflation forecasts based on a number of forecasting techniques from the literature using a panel of ten industrialized countries during the period from 1988 to 2007. To capture expected inflation, we develop a recursive filtering algorithm that extracts unexpected inflation from real interest rate data, even in the presence of diverse risks and a potential Mundell–Tobin-effect. The extracted unexpected inflation is compared to the forecasting errors of ten econometric forecasts. In addition to the standard AR(p) and ARMA(1,1) models, which are known to have the best performance on average, we also employ several Phillips curve-based approaches, VAR, dynamic factor models and two simple model averaging approaches. Finally, we show that the quality of the expectations clearly matches the quality of the forecasts derived with the techniques that are currently proposed for this purpose in the economic literature.