Quantifying Noise in Survey Expectations

49 Pages Posted: 17 Oct 2019 Last revised: 5 Aug 2022

See all articles by Artūras Juodis

Artūras Juodis

University of Groningen - Faculty of Economics and Business

Simas Kucinskas

Humboldt University of Berlin

Date Written: June 22, 2022

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

Suggested Citation

Juodis, Artūras and Kucinskas, Simas, Quantifying Noise in Survey Expectations (June 22, 2022). Available at SSRN: https://ssrn.com/abstract=3466196 or http://dx.doi.org/10.2139/ssrn.3466196

Artūras Juodis

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

Simas Kucinskas (Contact Author)

Humboldt University of Berlin

Dorotheenstrasse 1
Berlin, Berlin 10099
Germany

HOME PAGE: http://www.simaskucinskas.com

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