Støve, Bård () (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration) Tjøstheim, Dag () (Dept. of Mathematics, University of Bergen)
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 1/n. 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.
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Publisher Info
Paper provided by Department of Finance and Management Science, Norwegian School of Economics and Business Administration in its series Discussion Papers with number
2007/25.
Length: 33 pages Date of creation: 30 Nov 2007 Date of revision: Handle: RePEc:hhs:nhhfms:2007_025
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Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
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