Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/66297 
Erscheinungsjahr: 
1997
Schriftenreihe/Nr.: 
SFB 373 Discussion Paper No. 1997,11
Verlag: 
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, Berlin
Zusammenfassung: 
In many regression applications both the independent and dependent variables are measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different nonparametric techniques, regression splines and kernel estimation, of which both can be used in the presence of measurement error. Within the kernel regression context, we derive the limit distribution of the SIMEX estimate. With the regression spline technique, two different methods of estimations are used. The first method is the SIMEX algorithm which attempts to estimate the bias, and remove it. The second method is a structural approach, where one hypothesizes a distribution for the independent variable which depends on estimable parameters. A series of examples and simulations illustrate the methods.
Schlagwörter: 
Bootstrap
Measurement Error
Local Polynomial Regression
SIMEX
Asymptotic theory
Estimating Equations
Nonlinear Regression
Bandwidth Selection
Regression Splines
Sandwich Estimation
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Dokumentart: 
Working Paper

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