Arbeitspapier

Robust estimation in nonlinear regression and limited dependent variable models

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. On the other hand, semiparametric and nonparametric methods, which are not restricted by parametric assumptions, require more data and are less efficient. A third possible estimation approach is based on the theory of robust statistics, which builds upon parametric specification, but provides a methodology for designing misspecification-proof estimators. However, this concept, developed in statistics, has so far been applied almost exclusively to linear regression models. Therefore, I adapt some robust methods, such as least trimmed squares, to nonlinear and limited-dependent variable models. This paper presents the adapted robust estimators, proofs of their consistency, suitable computational methods, as well as examples of regression models which the proposed estimators can be applied to.

Language: Englisch

Bibliographic citation: Series: SFB 373 Discussion Paper ; No. 2001,100

Classification: Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

Subject: least trimmed squares
limited-dependent-variable models
nonlinear regression
robust estimation

Event: Geistige Schöpfung

(who): Čížek, Pavel

Event: Veröffentlichung

(who): Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

(where): Berlin

(when): 2001

Handle: http://hdl.handle.net/10419/62677

URN: urn:nbn:de:kobv:11-10051178

Last update: 20.09.2024, 8:22 AM CEST

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Object type

Arbeitspapier

Associated

Čížek, Pavel
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Time of origin

2001

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