A new consensus-based unemployment indicator
Oscar Claveria
Applied Economics Letters, 2019, vol. 26, issue 10, 812-817
Abstract:
In this study we present a novel approach to measure the level of consensus among agents’ expectations. The proposed framework allows us to design a positional indicator that gives the percentage of agreement between survey expectations. While other aggregation methods such as the balance, which is constructed as the difference between the percentages of respondents giving positive and negative replies, explicitly omit the neutral information, the proposed metric allows synthesizing the information coming from all response categories, including the percentage of respondents who do not expect any change. In order to assess the performance of the proposed measure of consensus, we compare its ability to track the evolution of unemployment to that of the balance in eight European countries. With this aim, we scale both measures to generate one-period ahead forecasts of the unemployment rate. We find that the consensus-based unemployment indicator outperforms the balance in all countries except Denmark and Sweden, which suggests that the level of agreement among agents’ expectations is a good predictor of unemployment.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (12)
Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2018.1497846 (text/html)
Access to full text is restricted to subscribers.
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:taf:apeclt:v:26:y:2019:i:10:p:812-817
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20
DOI: 10.1080/13504851.2018.1497846
Access Statistics for this article
Applied Economics Letters is currently edited by Anita Phillips
More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().