Robust estimation in nonlinear regression and limited dependent variable models
AbstractClassical 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. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2001,100.
Date of creation: 2001
Date of revision:
least trimmed squares; limited-dependent-variable models; nonlinear regression; robust estimation;
Other versions of this item:
- Pavel Cizek, 2002. "Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models," Econometrics 0203003, EconWPA.
- Pavel Cizek, 2001. "Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models," CERGE-EI Working Papers wp189, The Center for Economic Research and Graduate Education - Economic Institute, Prague.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Cizek, P., 2005. "Trimmed Likelihood-based Estimation in Binary Regression Models," Discussion Paper 2005-108, Tilburg University, Center for Economic Research.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.