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A convolution estimator for the density of nonlinear regression observations

Støve, Bård; Tjøstheim, Dag
Working paper
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URI
http://hdl.handle.net/11250/163901
Date
2007-11
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  • Discussion papers (FOR) [591]
Abstract
The problem of estimating an unknown density function has been widely studied. In this paper we present a convolution estimator for the density of the responses in a nonlinear regression model. The rate of convergence for the variance of the convolution estimator is of order n-1. This is faster than the rate for the kernel density method. The intuition behind this result is that the convolution estimator uses model information, and thus an improvement can be expected. We also derive the bias of the new estimator and conduct simulation experiments to check the finite sample properties. The proposed estimator performs substantially better than the kernel density estimator for well-behaved noise densities.
Publisher
Norwegian School of Economics and Business Administration. Department of Finance and Management Science
Series
Discussion paper
2007:25

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