Perfect classifiers in partial observability bivariate probit

Dale J. Poirier

Economics Letters, 2012, vol. 116, issue 3, 361-362

Abstract: There have been numerous applications of partial observability bivariate probit models. These models identify determinants of individual discrete outcomes when all that is observed are collective outcomes and individual characteristics. Numerical difficulties with their likelihood functions are commonly reported. This note looks at one aspect: the existence of perfect classifiers.

Keywords: Bayesian; Maximum likelihood; Prior (search for similar items in EconPapers)
JEL-codes: C1 C11 C2 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176512001383
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:ecolet:v:116:y:2012:i:3:p:361-362

DOI: 10.1016/j.econlet.2012.04.008

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().