Counting Biased Forecasters: An Application of Multiple Testing Techniques

Fabiana Gomez and David Pacini

Bristol Economics Discussion Papers from School of Economics, University of Bristol, UK

Abstract: We investigate the problem of counting biased forecasters among a group of unbiased and biased forecasters of macroeconomic variables. The innovation is to implement a procedure controlling for the expected proportion of unbiased forecasters that could be erroneously classified as biased (i.e., the false discovery rate). Monte Carlo exercises illustrate the relevance of controlling the false discovery rate in this context. Using data from the Survey of Professional Forecasters, we find that up to 7 out of 10 forecasters classified as biased by a procedure not controlling the false discovery rate may actually be unbiased.

Keywords: Biased Forecasters; Multiple Testing; False Discovery Rate. (search for similar items in EconPapers)
JEL-codes: C12 C23 E17 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2015-05-27
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.bristol.ac.uk/efm/media/workingpapers/w ... pdffiles/dp15661.pdf (application/pdf)

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:bri:uobdis:15/661

Access Statistics for this paper

More papers in Bristol Economics Discussion Papers from School of Economics, University of Bristol, UK
Bibliographic data for series maintained by Vicky Jackson ().