EconPapers    
Economics at your fingertips  
 

Worst-case estimation and asymptotic theory for models with unobservables

Jose Vidal-Sanz and Mercedes Esteban-Bravo ()

No 385, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: This paper proposes a worst-case approach for estimating econometric models containing unobservable variables. Worst-case estimators are robust against the averse effects of unobservables and, unlike the classical literature, there are no assumptions made about the statistical nature of the unobservables. This method should be seen as complementing standard methods; cautious modelers should compare different estimates to determine robust models. Limiting theory is obtained, and a Monte Carlo study of finite-sample properties is conducted. An economic application is included

Keywords: unobservable variables; robust estimation; minimax optimization; M-estimators; GMM-estimators (search for similar items in EconPapers)
JEL-codes: C13 C51 C60 (search for similar items in EconPapers)
Date: 2005-11-11
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://ideas.repec.org/p/cte/wbrepe/wb045518.html main text (text/html)

Related works:
Working Paper: Worst-case estimation and asymptotic theory for models with unobservables (2004) Downloads
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:sce:scecf5:385

Access Statistics for this paper

More papers in Computing in Economics and Finance 2005 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().

 
Page updated 2020-10-30
Handle: RePEc:sce:scecf5:385