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Qualitative and Asymptotic Performance of SNP Density Estimators

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Author Info
Victor Fenton
Gallant, A. Ronald ()
Abstract

The SNP estimator is the most convenient nonparametric method for simultaneously estimating the parameters of a nonlinear model and the density of a latent process by maximum likelihood. To determine if this convenience comes at a price, we assess the qualitative behavior of SNP in finite samples using the Marron--Wand test suite and verify theoretical convergence rates by Monte Carlo simulation. Our results suggest that there is no price for convenience because the SNP estimator is both qualitatively and asymptotically similar to the kernel estimator which is optimal.

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Publisher Info
Paper provided by Duke University, Department of Economics in its series Working Papers with number 96-17.

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Date of creation: 1996
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Publication status: Published in JOURNAL OF ECONOMETRICS, Vol. 74, 1996, pages 77-118
Handle: RePEc:duk:dukeec:96-17

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
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