Quantifying Noise in Survey Expectations
49 Pages Posted: 17 Oct 2019 Last revised: 5 Aug 2022
Date Written: June 22, 2022
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
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