Quantifying Noise

61 Pages Posted: 17 Oct 2019

See all articles by Artūras Juodis

Artūras Juodis

University of Groningen - Faculty of Economics and Business

Simas Kucinskas

Humboldt University of Berlin

Date Written: October 8, 2019

Abstract

Expectations affect economic decisions, and therefore inaccurate expectations are costly. Expectations can be wrong in ways that are systematic (bias) or unsystematic (noise). We provide a general method for quantifying the noise component. The method is based on the insight that theoretical models of expectation formation predict a factor structure for individual expectations. This insight leads to a widely applicable factor-based measurement procedure. Using data from professional forecasters, we find that noise is large and pervasive. Our findings have implications for forecast combination, macro models with incomplete information, and empirical research using micro data on expectations.

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 (October 8, 2019). 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

Register to save articles to
your library

Register

Paper statistics

Downloads
38
Abstract Views
184
PlumX Metrics