EconPapers    
Economics at your fingertips  
 

Belief elicitation with multiple point predictions

Markus Eyting and Patrick Schmidt

European Economic Review, 2021, vol. 135, issue C

Abstract: We propose a simple, incentive compatible procedure based on binarized linear scoring rules to elicit beliefs about real-valued outcomes - multiple point predictions. Simultaneously eliciting multiple point predictions with linear incentives reveals the subjective probability distribution without pre-defined intervals or probabilistic statements. We show that the approach is theoretically as robust as existing methods, while adapting flexibly to different beliefs. In a laboratory experiment, we compare our procedure to the standard approach of eliciting discrete probabilities on pre-defined intervals. We find that elicitation with multiple point predictions is faster, perceived as less difficult and more consistent with a subsequent decision. We further find that multiple point predictions are more accurate if beliefs vary between participants. Finally, we provide experimental evidence that pre-defined intervals anchor reports.

Keywords: Elicitation of subjective expectations; Partial identification; Quantiles; Experiment (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0014292121000532
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eecrev:v:135:y:2021:i:c:s0014292121000532

DOI: 10.1016/j.euroecorev.2021.103700

Access Statistics for this article

European Economic Review is currently edited by T.S. Eicher, A. Imrohoroglu, E. Leeper, J. Oechssler and M. Pesendorfer

More articles in European Economic Review from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2021-06-30
Handle: RePEc:eee:eecrev:v:135:y:2021:i:c:s0014292121000532