Robust Estimators for Simultaneous Equations Models

Posted: 20 Nov 2014

See all articles by Jaya Krishnakumar

Jaya Krishnakumar

University of Geneva

Elvezio Ronchetti

University of Geneva - Research Center for Statistics

Date Written: 1997

Abstract

This paper presents a class of robust estimators for linear and non-linear simultaneous equations models, which are a direct generalization of the maximum likelihood estimator. The new estimators are obtained as solutions of a generalized likelihood equation. They are resistant to deviations from the model distribution, to outlying observations, and to some model misspecifications. An optimality principle leads to the construction of an optimal robust estimator which is the best trade-off between efficiency at the model and robustness.

Keywords: Robustness; Influence function; M-estimators; Reduced form; Structural form; Nonlinear simultaneous equations; Full information maximum likelihood

JEL Classification: C30; C13

Suggested Citation

Krishnakumar, Jaya and Ronchetti, Elvezio, Robust Estimators for Simultaneous Equations Models (1997). Journal of Econometrics, Vol. 78, 1997, Available at SSRN: https://ssrn.com/abstract=2527112

Jaya Krishnakumar (Contact Author)

University of Geneva ( email )

40 Bd. du Pont d'Arve
Genève 4, CH - 1211
Switzerland

Elvezio Ronchetti

University of Geneva - Research Center for Statistics ( email )

Blv. Pont d'Arve 40
1211 Geneva 4
Switzerland

HOME PAGE: http://www.unige.ch/ses/metri/ronchetti/

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