Quantifying Noise in Survey Expectations

Abstract

Expectations affect economic decisions, and inaccurate expectations are costly. Expectations can be wrong due to either bias (systematic mistakes) or noise (un­systematic mistakes). We develop a framework for quantifying the level of noise in survey expectations. The method is based on the insight that theoretical models of expectation formation predict a factor structure for individual expectations. Us­ing data from professional forecasters, we find that the magnitude of noise is large (10%–30% of forecast MSE) and comparable to bias. We illustrate how our esti­mates can be applied to calibrate models with incomplete information and bound the effects of measurement error.

Keywords: expectation formation, factor models, measurement error, noise, panel data, subjective expectations

JEL Classification: C53, D83, D84, E70, G40