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Worst-case estimation and asymptotic theory for models with unobservables

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  • Jose M. Vidal-Sanz
  • Mercedes Esteban-Bravo

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

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 385.

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Date of creation: 11 Nov 2005
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Handle: RePEc:sce:scecf5:385

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Keywords: unobservable variables; robust estimation; minimax optimization; M-estimators; GMM-estimators;

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  1. Amemiya, Yasuo, 1985. "Instrumental variable estimator for the nonlinear errors-in-variables model," Journal of Econometrics, Elsevier, vol. 28(3), pages 273-289, June.
  2. Chamberlain, Gary & Griliches, Zvi, 1975. "Unobservables with a Variance-Components Structure: Ability, Schooling, and the Economic Success of Brothers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(2), pages 422-49, June.
  3. Chamberlain, Gary, 1977. "Education, income, and ability revisited," Journal of Econometrics, Elsevier, vol. 5(2), pages 241-257, March.
  4. Aigner, Dennis J. & Hsiao, Cheng & Kapteyn, Arie & Wansbeek, Tom, 1984. "Latent variable models in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 23, pages 1321-1393 Elsevier.
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