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  • NolteNoltePohlmeier2018_full

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 1, 2018 DOI: 10.1016/j.ijforecast.2018.07.007

    Accepted author manuscript, 1 MB, PDF document

    Embargo ends: 11/10/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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What determines forecasters’ forecasting errors?

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>01/2019
<mark>Journal</mark>International Journal of Forecasting
Issue number1
Volume35
Number of pages14
Pages (from-to)11-24
Publication statusPublished
Early online date11/10/18
Original languageEnglish

Abstract

This paper contributes to the growing body of literature in macroeconomics and finance on expectation formation and information processing by analyzing the relationship between expectation formation at the individual level and the prediction of macroeconomic aggregates. Using information from business tendency surveys, we present a new approach of analyzing forecasters’ qualitative forecasting errors. Based on a quantal response approach with misclassification, we define forecasters’ qualitative mispredictions in terms of deviations from the qualitative rational expectation forecast, and relate them to the individual and macro factors that are driving these mispredictions. Our approach permits a detailed analysis of individual forecasting decisions, allowing for the introduction of individual and economy-wide determinants that affect the individual forecasting error process.

Bibliographic note

This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 1, 2018 DOI: 10.1016/j.ijforecast.2018.07.007