Forecast Performance, Disagreement, and Heterogeneous Signal-to-Noise Ratios

We propose an imperfect information model for the expectations of macroeconomic forecasters that explains differences in average disagreement levels across forecasters by means of cross sectional heterogeneity in the variance of private noise signals. We show that the forecaster-specific signal-to-noise ratios determine both the average individual disagreement level and an individuals' forecast performance: forecasters with very noisy signals deviate strongly from the average forecasts and report forecasts with low accuracy. We take the model to the data by empirically testing for this implied correlation. Evidence based on data from the Surveys of Professional Forecasters for the US and for the Euro Area supports the model for short- and medium-run forecasts but rejects it based on its implications for long-run forecasts.