Nonparametric kernel and regression spline estimation in the presence of measurement error

J. D. Maca, Raymond J. Carroll and David Ruppert

No 1997,11, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Abstract: 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 When this happens, conventional parametric and nonparametric regression techniques are no longer valid.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.

Keywords: Bootstrap; Measurement Error; Local Polynomial Regression; SIMEX; Asymptotic theory; Estimating Equations; Nonlinear Regression; Bandwidth Selection; Regression Splines; Sandwich Estimation (search for similar items in EconPapers)
Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:199711

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