Evolution of Forecast Disagreement in a Bayesian Learning Model

We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: i) the initial prior beliefs, ii) the weights attached on priors, and iii) interpreting public information. The fixed-target, multi-horizon, cross-country feature of the panel data allows us to estimate the relative importance of each component precisely. The first component explains nearly all to 30% of forecast disagreement as the horizon decreases from 24 months to 1 month. This finding firmly establishes the role of initial prior beliefs in generating expectation stickiness. We find the second component to have barely any effect on the evolution of forecast disagreement among experts. The importance of the third component increases from almost nothing to 70% as the horizon gets shorter via its interaction with the quality of the incoming news. We propose a new test of forecast efficiency in the context of Bayesian information processing and find significant heterogeneity in the nature of inefficiency across horizons and countries