We propose a bias correction for the average of a set of individual inflation expectations considering the possibility that intercept and slope biases may vary over time. We proceed in two ways. Firstly, we consider estimations based on rolling windows. Secondly, we employ a state-space model to obtain time-varying intercept and slope biases using the recursiveness of the Kalman filter. The latter approach has the advantage of circumventing the choice of the rolling window size. We also proceed with estimations based on expanding windows, a procedure that is close to what has been done in the literature. We achieve good forecast performance for models based on small rolling windows for shorter and intermediate forecast horizons. In turn, a state-space model that includes corrections for intercept and slope biases varying over time tends to perform slightly worse than procedures based on rolling windows.
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