Worst-case estimation and asymptotic theory for models with unobservables
AbstractThis 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
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 385.
Date of creation: 11 Nov 2005
Date of revision:
unobservable variables; robust estimation; minimax optimization; M-estimators; GMM-estimators;
Other versions of this item:
- Mercedes Esteban-Bravo & Jose M. Vidal-Sanz, 2004. "Worst-Case Estimation And Asymptotic Theory For Models With Unobservables," Business Economics Working Papers wb045518, Universidad Carlos III, Departamento de Economía de la Empresa.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
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