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
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)
New Economics Papers: this item is included in nep-ecm
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Working Paper: Worst-case estimation and asymptotic theory for models with unobservables (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:385
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