How do we learn about the long run?

Using a novel and unique panel dataset of individual-level professional forecasts at short, medium, and very-long horizons, we provide new stylized facts about survey forecasts. We present direct evidence that forecasters use multivariate models in an environment with imperfect information about the current state, leading to heterogenous non-stationary expectations about the long run. We show forecast revisions are consistent with the predictions of a multivariate unobserved trend and cycle model. Our results suggest models of expectations formation which are either univariate, stationary, or both, are inherently misspecified and that macroeconomic modelling should reconsider the conventional assumption that agents operate in a well-understood stationary environment.