Learning in Real Time: Theory and Empirical Evidence from the Term Structure of Survey Forecasts

We develop a theoretical framework for understanding how agents form expectations about economic variables with a partially predictable component. Our model incorporates the e¤ect of measurement errors and heterogeneity in individual forecasters’prior beliefs and their information signals and also accounts for agents’learning in real time about past, current and future values of economic variables. We use the model to develop insights into the term structure of forecast errors, and test its implications on a data set comprising survey forecasts of annual GDP growth and in‡ation with horizons ranging from 1 to 24 months. The model is found to closely match the term structure of forecast errors for consensus beliefs and is able to replicate the cross-sectional dispersion in forecasts of GDP growth but not for in‡ation the latter appearing to be too high in the data at short horizons. Our analysis also suggests that agents systematically underestimated the persistent component of GDP growth but overestimated it for in‡ation during most of the 1990s. We thank Roy Batchelor, Steve Cecchetti, Jonas Dovern, Mike McCracken, Hashem Pesaran, Michela Verardo, Mark Watson and seminar participants at the Board of Governors of the Federal Reserve, Cambridge, City University London, Duke, London School of Economics, NBER Summer Institute, Universite Libre Bruxelles (ECARES), Tilburg and Stanford (SITE workshop) for helpful comments and suggestions. Email: [email protected] and [email protected].